Fire, Fuel Treatments, and

Ecological Restoration:

Conference Proceedings

April 16-18, 2002

Fort Collins, CO

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Proceedings

RMRS-P-29

June 2003
Omi, Philip N.; Joyce, Linda A., technical editors. 2003. Fire, fuel treatments, and

ecological restoration: Conference proceedings; 2002 16-18 April; Fort Collins,

CO. Proceedings RMRS-P-29. Fort Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Research Station. 475 p.

Recent fires have spawned intense interest in fuel treatment and ecological restoration activities. Scientists and land managers have been advocating these activities

for years, and the recent fires have provided incentives for federal, state, and local

entities to move ahead with ambitious hazard reduction and restoration projects.

Recent fires also have increased public awareness about the risks and hazards of

living in wild areas. The scientific basis for ecological restoration and fuel treatment

activities is growing, but remains largely unsubstantiated, with isolated exceptions. Over

300 participants from all over the United States convened in Ft. Collins, Colorado,

to learn from 90 oral and poster presentations.

Sponsors

• USDA Forest Service • Joint Fire Sciences Program

• Colorado State Forest Service • Society of American Foresters

• Colorado State University • Western Forest Fire Research Center (WESTFIRE)

Conference Coordinators

• Dr. Phil Omi, Professor, Department of Forest Sciences, Colorado State University

• Dr. Linda Joyce, Research Project Leader, Rocky Mountain Research Station,

USDA Forest Service

Editors’ Note

Papers presented from the conference were subjected to peer technical review. The

views expressed are those of the presenters.

Cover photo: Biscuit Fire, Siskiyou National Forest, 2002. Photo by Thomas Iraci,

USDA Forest Service. Courtesy of Fire Management Today magazine.

You may order additional copies by sending your mailing information in

label form through one of the following media. Please specify the publication title and number.

Telephone (970) 498-1392

FAX (970) 498-1396

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526

Rocky Mountain Research Station

Natural Resources Research Center

2150 Centre Avenue, Building A

Fort Collins, CO 80526
i

Contents

Foreword: Conference on Fire, Fuel Treatments, and Ecological Restoration:

Proper Place, Appropriate Time ........................................................................ 1

Philip N. Omi and Linda A. Joyce, Conference Co-Coordinators

Fuel Treatment Performance and Fire Hazard Reduction

Fuel Treatments: Opening Remarks................................................................... 5

Wayne D. Shepperd, Research Forester, Rocky Mountain Research Station;

and Sarah Gallup, Fuels/Fire Planner, Arapaho-Roosevelt National Forest

Performance of Fuel Treatments Subjected to Wildfires ........................................ 7

Erik J. Martinson and Philip N. Omi, Western Forest Fire Research Center,

Department of Forest Sciences, Colorado State University

Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida ............ 15

John M. Pye, Jeffrey P. Prestemon, David T. Butry, and Karen Lee Abt,

Southern Research Station, USDA Forest Service

Fire Hazard and Silvicultural Systems: 25 Years of Experience

From the Sierra Nevada ................................................................................... 27

Scott Stephens and Jason Moghaddas, Division of Forest Science,

Department of Environmental Science, Policy, and Management,

University of California

Canopy Fuel Treatment Standards for the Wildland-Urban Interface ................... 29

Joe H. Scott, Systems for Environmental Management

Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico ................ 39

Carl E. Fiedler, School of Forestry, University of Montana; and Charles E.

Keegan, Bureau of Business and Economic Research, University of Montana

Definition of a Fire Behavior Model Evaluation Protocol:

A Case Study Application to Crown Fire Behavior Models .............................. 49

Miguel G. Cruz, Associação para o Desenvolvimento da Aerodinâmica

Industrial and School of Forestry, University of Montana; Martin E.

Alexander, Canadian Forest Service, Northern Forestry Centre, Edmonton,

Alberta, Canada; and Ronald H. Wakimoto, School of Forestry, University of

Montana

Fire, Fuel Treatments, and Ecological Restoration:

Conference Proceedings

April 16-18, 2002

Fort Collins, Colorado
ii

In Situ Soil Temperature and Heat Flux Measurements During Controlled

Surface Burns at a Southern Colorado Forest Site ............................................ 69

W.J. Massman, J.M. Frank, and W.D. Shepperd, USDA Forest Service, Rocky

Mountain Research Station; and M.J. Platten, USDA Forest Service, Manitou

Experimental Forest

Restoration Case Studies and Ecosystem Effects

Ecological Restoration Thinning of Ponderosa Pine Ecosystems:

Alternative Treatment Outcomes Vary Widely ................................................ 91

W. Wallace Covington, Ecological Restoration Institute, Northern

Arizona University

Aspen Response to Prescribed Fire, Mechanical Treatments, and

Ungulate Herbivory ........................................................................................ 93

Steve Kilpatrick and Dean Clause, Wyoming Game & Fish

Department; and Dave Scott, USDA Forest Service,

Bridger-Teton National Forest

Experimental Thinning and Burning of Ponderosa Pine Forests in

Southwestern Colorado: Effects on Canopy Structure,

Understory Composition, and Fuels .............................................................. 103

W.H. Romme, Department of Forest Sciences, Colorado State

University; M.L. Floyd-Hanna and D.D. Hanna, Environmental Studies

Program, Prescott College; and Phil Kemp, USDA Forest Service,

San Juan National Forest

Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentaryand Volcanic-Derived Soils of the Mogollon Rim, Arizona ........................... 105

Daniel G. Neary and Steven T. Overby, USDA Forest Service,

Rocky Mountain Research Station; and Sally M. Haase, USDA

Forest Service, Pacific Southwest Research Station

Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project ............................ 117

Ken Moore, Bob Davis, and Timothy Duck, Parashant National

Monument

Wildlife Responses to Alternative Fire Management Treatments:

The National Fire/Fire Surrogate Study Approach .......................................... 133

Steve Zack and Kerry Farris, Wildlife Conservation Society

Comparing Two Methods of Identifying Ecological Restoration

Opportunities ................................................................................................ 135

Jimmie D. Chew, USDA Forest Service, Rocky Mountain Research

Station

The Post-Burning Response of Bark Beetles to Prescribed

Burning Treatments ....................................................................................... 143

David J. Ganz and Donald L. Dahlsten, Department of Environmental

Science, Policy and Management, University of California; and Patrick J.

Shea, USDA Forest Service, Pacific Southwest Research Station

Root Pathogens and Fire: Silvicultural Interactions in “Exotic” Ecosystems ........ 159

William J. Otrosina, Susana S. Sung, Charles H. Walkinshaw, and

Brian T. Sullivan, USDA Forest Service, Institute for Tree-Root Biology
iii

Treatment—Economic

Social Sciences and the Economics of Moderation in Fuels Treatment .............. 163

Douglas B. Rideout, Forest Economics, Colorado State University

Costs for Reducing Fuels in Colorado Forest Restoration Projects ..................... 167

Dennis L. Lynch and Kurt Mackes, Department of Forest, Rangeland,

and Watershed Stewardship, Colorado State University

The Effects of Fire on Hiking Demand: A Travel Cost Study of

Colorado and Montana ................................................................................. 177

Hayley Hesseln, School of Forestry, University of Montana;

John B. Loomis, Department of Agricultural and Resource Economics,

Colorado State University; and Armando González-Cabán, USDA Forest

Service, Forest Fire Lab, Pacific Southwest Research Station

Linking GIS and Recreation Demand Models to Estimate the

Economic Value of Using Fire to Improve Deer Habitat ................................ 187

John Loomis, Department of Agricultural and Resource Economics,

Colorado State University; Armando González-Cabán, Forest Fire Lab,

USDA Forest Service, Pacific Southwest Station; and Dana Griffin and

Ellen Wu, Department of Agricultural and Resource Economics,

Colorado State University

Co-Firing Wood Biomass With Coal at the Cañon City Power Plant ................. 203

Daniel Prokupets, Colorado State Forest Service; and Kurt Mackes

and Skip Smith, Department of Forest, Rangeland, and Watershed

Stewardship, Colorado State University

Treatment—Social Issues

Fire Social Science Research: Opening Remarks............................................... 209

Antony S. Cheng, Department of Forest Sciences, Colorado State

University

People and Fire in Western Colorado: Methods of Engaging Stakeholders........ 213

Sam Burns, Office of Community Services, Fort Lewis College;

Chuck Sperry, The Rocky Mountain Center for Economic Democracy;

and Ron Hodgson, Fire & Aviation Management, Bureau of Land

Management

From Analysis Paralysis to Agency-Community Collaboration in

Fuels Reduction for Fire Restoration: A Success Story ................................... 225

Timothy Ingalsbee, Western Fire Ecology Center, American

Lands Alliance

Hazardous Fuel Reduction in the Blue Mountains:

Public Attitudes and Opinions ...................................................................... 241

Eric Toman , Department of Forest Resources, Oregon State

University; and Bruce Shindler, Department of Forest Resources,

Oregon State University

Fire, Fuel, and Restoration Priorities of the Forest Conservation Community ..... 255

Gregory H. Aplet, The Wilderness Society
iv

Fire Regime Considerations

Key Issues in Fire Regime Research for Fuels Management and

Ecological Restoration .................................................................................... 259

Thomas T. Veblen, Department of Geography, University of Colorado

Lessons From the Fires of 2000: Post-Fire Heterogeneity in

Ponderosa Pine Forests................................................................................... 277

Natasha B. Kotliar and Sandra L. Haire, US Geological Survey,

Fort Collins Science Center; and Carl H. Key, US Geological Survey,

USGS - Glacier Field Station Science Center, Glacier National Park

Mapping the Cheatgrass-Caused Departure From Historical

Natural Fire Regimes in the Great Basin, USA ................................................ 281

James P. Menakis, Fire Effects Unit, Fire Science Laboratory, USDA

Forest Service, Rocky Mountain Research Station; Dianne Osborne,

National Science and Technology Center, Bureau of Land Management;

and Melanie Miller, Bureau of Land Management

Determining the Spatial Extent of Historical Fires With

Geostatistics in Northern Lower Michigan ..................................................... 289

Ann L. Maclean, School of Forest Resources and Environmental

Sciences, Michigan Technological University; and David T. Cleland,

USDA Forest Service, North Central Research Station

Scaling Rules and Probability Models for Surface Fire Regimes in

Ponderosa Pine Forests................................................................................... 301

Donald A. Falk and Thomas W. Swetnam, Laboratory of Tree Ring

Research, University of Arizona

Uncertainty in Fire History and Restoration of Ponderosa Pine Forests

in the Western United States .......................................................................... 319

William L. Baker and Donna S. Ehle, Department of Geography

and Recreation, University of Wyoming

Ancient Piñon-Juniper Forests of Mesa Verde and the West:

A Cautionary Note for Forest Restoration Programs........................................ 335

William H. Romme, Department of Forest Sciences, Colorado State

University; and Lisa Floyd-Hanna and David D. Hanna,

Prescott College

Landscape Planning

Expectation and Evaluation of Fuel Management Objectives ............................. 353

Mark A. Finney and Jack D. Cohen, USDA Forest Service Fire

Sciences Laboratory, Rocky Mountain Research Station

Scheduling Removals for Fuels Management ..................................................... 367

John Hof, USDA Forest Service, Rocky Mountain Research Station; and

Philip Omi, Department of Forest Sciences, Colorado State University

Wildland Fire Use: A Wilderness Perspective on Fuel Management .................. 379

Carol Miller, Aldo Leopold Wilderness Research Institute,

USDA Forest Service, Rocky Mountain Research Station
v

Modeling the Effects of Fuel Treatments for the Southern Utah

Fuel Management Demonstration Project ...................................................... 387

Donald Long, Kevin Ryan, Rick Stratton, Ed Mathews, Joe Scott,

Maureen Mislivets, Melanie Miller, and Sharon Hood, Fire

Sciences Lab, USDA Forest Service, Rocky Mountain Research Station

Fire Regime Condition Class and Associated Data for Fire and

Fuels Planning: Methods and Applications..................................................... 397

Wendel J. Hann, USDA Forest Service Fire Management; and

Diane J. Strohm, Pike-San Isabel and Comanche-Cimarron National

Forests and Grasslands

Closing Comments

Fire, Fuel Treatments, and Ecological Restoration—Proper Place,

Appropriate Time ........................................................................................... 435

G. Thomas Zimmerman, National Park Service, National Interagency

Fire Center

Poster Abstracts .............................................................. 445

Author Contact List......................................................... 471
Fuel Treatment Performance

and Fire Hazard Reduction

Landscape Planning

Poster Abstracts

Restoration Case Studies

and Ecosystem Effects

Treatment—Economic

Treatment—Social Issues

Fire Regime Considerations
USDA Forest Service Proceedings RMRS-P-29. 2003. 1

Foreword

Conference on Fire, Fuel Treatments, and

Ecological Restoration: Proper Place,

Appropriate Time

Philip N. Omi and Linda A. Joyce, Conference Co-Coordinators

Fuel treatments and ecological restoration are not novel concepts to the

land manager. Fuels have been manipulated in southern California since

the 1930s, and ecological restoration has been advocated since the 1968

Leopold report re-defined the need for manipulating national park landscapes.

Even earlier, advocates for light-burning of the nation’s forests or for manipulation of wildlife habitat urged public and private land stewards to restore fire

to regional landscapes. Perhaps if we had heeded their words we might have

been spared the litany of “worst” fire seasons, such as 1988, 1994, 2000, and

2002. Of course, new circumstances have surfaced in response to these fire

seasons, most notably the healthy forests initiative, national fire plan, and cohesive strategies for dealing with the nation’s wildfire problems. Unlike previous

agency mandates, the national fire plan has been accompanied with dollars to

perform treatments—and dollars for which we are owed accountability. Still,

numerous questions remain unresolved about the proper place and appropriate time for fuel treatments, to say nothing about suspicions that ecological

restoration might be a cover-up for getting the cut out of the nation’s forests

or other nefarious acts. In short, this conference could not have been bettertimed in terms of serving the public interest!

This conference was born of a Joint Fire Science research project at Colorado State University looking at the status of our knowledge regarding fire

regimes and fuel treatments. A glance through the contents of these proceedings reveals a much broader perspective—for this we can thank all who

responded to our initial call for abstracts. The core of the technical program

for this conference focused on fuel treatment for fire hazard reduction, and

ecological restoration case studies (including ecosystem effects). In addition,

we are pleased to include papers that examine treatment economics and social

issues, fire regime considerations, and landscape planning perspectives. In

addition to these same topical areas, poster abstracts cover studies of wildfire

effects, methodological considerations, and monitoring guidelines. In total,

we expect that this collection of papers represent a unique and timely contribution to the literature. Further we hope that these proceedings will provide

an important reference for future efforts.

Levels of interest for this conference (i.e., abstracts, registrations, general

inquiries) exceeded our greatest expectations, especially since we started planning shortly after September 11, 2001. Participants included nearly 300

scientists, land managers, students, and interested individuals from public and

private sectors. Our hope is that the papers included here will shape the future of fuel manipulation and ecological restoration programs here and around

the globe.
2 USDA Forest Service Proceedings RMRS-P-29. 2003.

Special thanks are owed to the steering committee that provided invaluable

assistance with planning and implementing conference activities. Members of

the steering committee are noted below:

Steering Committee

Phil Omi, Conference Co-Coordinator, Department of Forest Sciences,

Colorado State University, Fort Collins, CO, 80523

Linda Joyce, Conference Co-Coordinator, USDA Forest Service, Rocky

Mountain Research Station, 240 W. Prospect, Fort Collins, CO, 80526

Mike da Luz, USDA Forest Service, P.O. Box 25127, Lakewood, CO, 80526

Lane Eskew, USDA Forest Service, Rocky Mountain Research Station, 240

W. Prospect, Fort Collins, CO, 80526

Paul Gleason (deceased),* Department of Forest Sciences, Colorado State

University, Fort Collins, CO, 80523

Christy Higgason, Department of Forest Sciences, Colorado State,

University, Fort Collins, CO, 80523

Molly Hunter, Department of Forest Sciences, Colorado State University, Fort

Collins, CO, 80523

Mohammed Kalkhan, Natural Resource Ecology Laboratory, Colorado State

University, Fort Collins, CO, 80523

Merrill Kaufmann, USDA Forest Service, Rocky Mountain Research Station,

240 W. Prospect, Fort Collins, CO, 80526

Kurt Mackes, Department of Forest Sciences, Colorado State University, Fort

Collins, CO, 80523

Karen Martinez, Colorado State University Conference Services, Colorado

State University, Fort Collins, CO 80523

Erik Martinson, Department of Forest Sciences, Colorado State University,

Fort Collins, CO, 80523

Bill Romme, Department of Forest Sciences, Colorado State University, Fort

Collins, CO, 80523

Susan Stafford, Department of Forest Sciences, Colorado State University,

Fort Collins, CO, 80523

*We add a special tribute to the memory of Paul Gleason, who served on our steering

committee and also coordinated the four conference field trips. Paul had a special

passion for the tasks confronting fire and fuels managers. He was an avid reader and

discussant about fire research and an eloquent spokesperson for firefighter safety.

Above all he will be remembered for his contagious enthusiasm and respect for the

land.
USDA Forest Service Proceedings RMRS-P-29. 2003. 3

Fuel Treatment Performance

and Fire Hazard Reduction
4 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 5

Fuel Treatments: Opening Remarks

Wayne D. Shepperd1 and Sarah Gallup2

One person most often quoted (ironically by both sides) in the

ecowars of land management policy on federal lands is Aldo Leopold.

His classic book on wildlife management was the text for the undergraduate

Wildlife Management class at CSU many years ago and sits on many office

bookshelves. In it he examined the habitat needs of various upland game species and concluded that many of these animals and birds thrive where there is

an abundance of what he termed “edge,” where a variety of habitat conditions

were available. In today’s parlance, we might refer to that as “habitat diversity.” Different species of plants and animals require different conditions with

no single condition being optimal for all. Therefore, a variety of conditions

within a landscape are likely to benefit most flora and fauna occurring within

it. Such diversity is also beneficial for processes that are active within a landscape. If we take the next step and refer to landscapes containing flora, fauna,

and processes as ecosystems, we might also refer to those containing sufficient

diversity as being in a “properly functioning condition.”

Regardless of the term, the thought behind the idea is every bit as valid

today as when Leopold observed it – too much of any one condition in an

ecosystem is not good. It is as true for plants as it is for animals in an ecosystem. And, it is also true for the disturbance processes that affect ecosystems,

including fire. The key for providing for all components of an ecosystem is to

maximize diversity – to make room for all. Exactly how we do that on the

ground will depend upon the particular ecosystem we are dealing with, the

particular mix of conditions that currently exist within it, and the particular

species or process of concern. No one approach is ever likely to fit all circumstances. Remembering this will be helpful to readers of this section as we learn

in the following papers about various approaches that have been used to reduce the risk of unacceptable fire in forest ecosystems. No single technique is

likely to be perfect for all situations. Just as he said when he advised us to

“save all of the pieces” when tinkering with ecosystems, Aldo Leopold would

likely also advise us to “bring more than one wrench” to do the job. So, let’s

see what’s in the tool kit!

1Research Forester, Rocky Mountain

Research Station, Fort Collins, CO.

2

Fuels/Fire Planner, Arapaho-Roosevelt

National Forest, Fort Collins, CO.
6 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 7

Performance of Fuel Treatments

Subjected to Wildfires

Erik J. Martinson1 and Philip N. Omi1

Abstract—Fire severity was evaluated in eight recent wildfires with standardized

methods in adjacent treated and untreated stands. Sampled sites occurred in a variety

of conifer forests throughout the Western United States. Treatments included reduction

of surface fuels and crown fuels, both in isolation and in combination. Synthesis of

our results indicates that treatment effectiveness is related to differences in tree size

(mean diameter) between treated and untreated stands (p<0.001), as well as estimated

historic fire frequency (p<0.1). Our results suggest that fuel treatments will be most

effective when they complement ecosystem restoration objectives, such as the removal

of small trees from ecosystems that historically experienced frequent fire.

Introduction

Treatments to mitigate fuel accumulation and fire hazard have long been

advocated (Weaver 1943). Federal land management agencies have

greatly expanded fuel treatment programs in response to increased public attention on wildfire hazards. The unprecedented scale of current fuel treatment

activities has intensified debate regarding their means, objectives, and outcomes. Some interest groups maintain that fuel treatments via mechanical

thinning are a disguise to expedite timber harvest. Others wonder if potential

negative impacts of fuel treatments (e.g., smoke production, exotic invasions,

soil damage) outweigh any benefits. Some question whether fuel treatments

even decrease fire potential.

Theory does suggest that fire intensity may be exacerbated by fuel treatments (Agee 1996). Canopy reduction exposes surface fuels to increased solar

radiation, which would be expected to lower fuel moisture content and promote production of fine herbaceous fuels. Surface fuels may also be exposed

to higher wind speeds, accelerating both desiccation and heat transfer. Treatments that include prescribed burning may increase nutrient availability and

further stimulate production of fine fuels. All these factors facilitate combustion, increase rates of heat release, and increase surface fire intensity.

However, theory also indicates that treatments can reduce the likelihood of

extreme fire behavior involving forest canopies. Crown fire initiation and spread

depends on vertical and horizontal fuel continuities (Van Wagner 1977) that

are typically reduced by treatment. Thus, treatments that reduce canopy fuels

may increase and decrease fire hazard simultaneously. Justifications for expansion of fuel treatment practices are therefore tenuous without empirical

assessments of their performance in wildfires. However, the question of fuel

treatment effectiveness has received surprisingly little scientific attention.

1Western Forest Fire Research Center,

Department of Forest Sciences, Colorado

State University, Fort Collins, CO.
8 USDA Forest Service Proceedings RMRS-P-29. 2003.

Previous Research

An exhaustive literature search for evidence of fuel treatment effectiveness

in the United States uncovered just 31 such publications since 1955. Some

perspective on the void this represents is gained by considering the number of

citations in the Fire Effects Information System (more than 25,000 op. cit.

Fischer et al. 1996). Further perspective is gained when these publications are

organized by the methods used to assess treatment effectiveness. Fuel treatments in more than half of these publications were not tested by actual wildfires

and the treatments themselves are also hypothetical in nearly a third of them.

Among the 14 studies of treatments subjected to actual wildfires, just five

quantify how fuels were changed by the treatment: a necessity if effective guidance is to be provided for future fuels management. Nonetheless, these

publications do indicate with near unanimity that fuel treatments mitigate

wildfire behavior and effects (table 1).

However, even among the 10 studies that assess the severity (as opposed to

size) of actual wildfires, comparisons are complicated by lack of consistency

both in the criteria for evaluating fire severity and in definitions of fuel treatments and controls. Several studies evaluate damage to tree crowns, but some

authors define severe damage as more than 50% scorch, while others use 100%

scorch or complete consumption as their highest rating. Treatments involve

commercial harvest in several of the studies with activity fuels subsequently

burned. Some of these assess treatment effectiveness with comparisons to areas where no management activity occurred; others use harvested areas where

slash was left untreated. One study (Weatherspoon and Skinner 1995) makes

comparisons to both types of areas, allowing interpretation of treatment effects as either positive or negative. This was the only study found that provides

any indication that fuel treatments may be ineffective. But the weight of evidence represented in these few studies is far from overwhelming, especially

since sampling designs are inadequately described in the earlier publications.

Thus, neither existing theory nor available empirical evidence provides much

clarity on the question of fuel treatments and the conditions that influence

their effectiveness when tested by wildfire.

Martinson and Omi Performance of Fuel Treatments Subjected to Wildfires

Table 1—Characteristics and findings of published studies that document the performance of fuel treatments in

actual wildfires.

Study Treatment Control Response Directiona

Moore et al. 1955 Prescribed burn No activity Crown damage ◗

Cumming 1964 Prescribed burn No activity Tree mortality ◗

Wagle and Eakle 1979 Prescribed burn No activity Live tree density ▼

Van Wagner 1968 Thin and prune No activity Tree survival ▼

Agee 1996 Thin and burn No activity Crown fire ◗

Oucalt and Wade 1999 Thin and burn Thin Tree mortality ◗

Vihanek and Ottmar 1993 Harvest and burn Harvest Soil damage ◗

Hall et al. 1999 Harvest and burn Harvest Crown fire ◗

Weatherspoon and Skinner 1995 Harvest and burn No activity Crown damage ▼

Omi and Kalabokidis 1991 Harvest and burn No activity Crown damage ◗

aDirection indicates the amount of the measured response in the treated areas relative to that measured in the untreated control

areas. For example, less crown damage was evident in the treated area than in the control area of the Moore et al. (1955) study.
USDA Forest Service Proceedings RMRS-P-29. 2003. 9

Objectives

A project was initiated in 1995 to begin filling the research void on fuel

treatment effectiveness. Eight wildfires have been investigated to date with

details previously described (for details see Pollet and Omi 2002; Omi and

Martinson 2002). Here we use meta-analytical methods to synthesize the results from these eight study sites. We investigated the ability of several variables

(type, age, and intensity of treatments and the historic frequency of fire in the

treated ecosystems) to explain differences among study sites in observed treatment effects.

Methods

We identified potential study sites for this research by advertising our interest at professional conferences and over the internet, networking with federal

land managers, and initiating contact after large wildfires in areas known to

have an active fuels management program. Thirty-eight wildfire areas were

considered for sampling, but most of these failed to meet our selection criteria.

Potential study sites were restricted to wildfires that included adjacent treated

and untreated areas within the perimeter and where treatment histories were

documented and spatially explicit. We chose a narrow definition of fuel treatment that included only non-commercial or pre-commercial activities involving

mechanical thinning (i.e., “low thinning”), debris removal, and/or broadcast

burning with moderation of wildfire potential as a stated objective. Areas were

defined as “untreated” if they had received no management action within the

last 20 years, while treatments were applied within the last 10 years. We avoided

areas where significant barriers (e.g., cliffs, major roads or drainages) or suppression activities likely impeded fire spread, as well as areas where post-fire

salvage activities had taken place or were imminent. We further restricted our

sampling visits to forested ecosystems, since these are where treatments are

most often applied (Morrison et al. 2001) and where our methods are most

applicable. Ten sites met our selection criteria, but two of these were excluded

due to their proximity to areas we had sampled previously. Characteristics of

the eight sampled sites are provided in table 2.

Data were collected at all sites from variable radius plots (Avery and Burkhart

1994) in adjacent treated and untreated stands. Measurements included stand

density and basal area, tree diameter and height to pre-fire live crown, height

Performance of Fuel Treatments Subjected to Wildfires Martinson and Omi

Table 2—Characteristics of the eight study sites included in the synthesis.

Site Treatment type Treatment age (yr) Vegetation Historic MFIa

’94 Webb fire, MT Prescribed burn 4 Ponderosa 14

’94 Tyee fire, WA Thin and burn 10 Ponderosa 22

’94 Cottonwood fire, CA Thin, slash removed 4 Ponderosa 28

’96 Hochderffer fire, AZ Thin and burn 1 Ponderosa 16

’99 Fountainebleau fire, MS Prescribed burns 1 Slash pine 9

’99 Megram fire, CA Pile and burn 2 Mixed conifer 59

’00 Cerro Grande fire, NM Thin 1 Ponderosa 17

Thin and burn 4 Ponderosa 17

’00 Hi Meadow fire, CO Prescribed burn 1, 3, 5 Mixed conifer 52

Thin 9 Mixed conifer 52

a Historic mean fire interval (MFI) was estimated for each site from the nearest available fire history information.
10 USDA Forest Service Proceedings RMRS-P-29. 2003.

of needle scorch and bole char, percent crown volume scorch, and standardized ratings for stand damage and depth of ground char (Omi and Martinson

2002).

We used the standard meta-analytical software Metawin (Rosenberg et al.

2000) to relate fuel treatment effect sizes (i.e., Hedge’s standardized mean

difference, see Rosenberg et al. 2000) on percent crown volume scorch to

each of several site characteristics. These included the type of treatment (in

terms of the fuel stratum treated: canopy, surface, or both), treatment age

when tested by wildfire (grouped into categories of 1 year, 2 to 4 years, and 5

to 10 years), standardized mean differences in tree densities and diameters

between treated and untreated areas, and the estimated historic fire frequency

of each site.

Historic fire frequency was estimated for each study site from proximal fire

history studies. We identified and selected applicable fire histories from those

included in a quantitative synthesis of fire history information (for details see

Martinson and Omi, in press). Historic fire frequency was standardized from

each fire history by calculating the inverse of the average annual point-specific

probability of fire in the period 1710-1779. We estimated the historic fire

frequency for each site as a weighted (inversely proportional to variance) average of fire frequencies calculated from the nearest (in terms of latitude,

longitude, and elevation) available fire histories.

We employed parametric mixed-effects models in all analyses. Comparison

of the size of the random variance component (i.e., variation not explained by

sampling error at each location, q

2

) when an explanatory variable is included

in the analysis to its size when the predictor is left out provides a measure of

the explanatory power (r2

MA) of a parametric mixed effects meta-analytical

model (Cooper and Hedges 1994):

( )( )

( ) no predictor

no predictor predictor included

rMA 2

2 2

2

q

q q

s

s -s = [1]

For comparison, we also report the traditional coefficient of determination

(r2

) produced by ordinary regression and analysis of variance, though interpretation of this value is ambiguous in a meta-analysis since it describes the

relationship among mean differences, but ignores sampling error. Since pseudoreplication (Hurlbert 1984) was unavoidable at several of the study sites, we

did not employ the meta-analytical convention of weighting individual studies

by their variance; all sites were given equal weight.

Results and Discussion

Similar to findings from previous research, results from our investigations

unanimously indicate that fuel treatments reduced wildfire severity in treated

areas. Crown volume scorch averaged 38% in treated areas across the eight

study sites, versus 84.5% in untreated areas. Nonetheless, treatment effects

among the study sites were variable in their significance. Meta-analysis suggests that much of the variability in the size of treatment effects can be explained

by site characteristics, particularly the differences in mean tree diameter between treated and untreated areas (table 3). Mean tree diameter in treated

areas was 33.0 cm compared to 23.8 cm in untreated areas. Treatments that

increase the average diameter of residual trees through removal of the smallest

sq

Martinson and Omi Performance of Fuel Treatments Subjected to Wildfires
USDA Forest Service Proceedings RMRS-P-29. 2003. 11

stems appear most effective. This result illustrates the importance of distinguishing fuel treatments from those silvicultural activities that “thin from above”

through removal of the largest trees from a stand (Graham et al. 1999).

Vegetation in untreated areas was denser, on average, than in treated areas:

931 versus 319 trees/ha. But tree density differences between treated and

untreated areas were insignificant as a predictor of fire severity differences

among our study sites. This could be an artifact of the sampling method that

we employed, since variable radius plots may provide inaccurate density estimates for small trees (Stage and Rennie 1994). However, they are more efficient

than fixed area plots for sampling the larger trees that are more informative

recorders of fire intensity. Nonetheless, the relative insignificance of tree density in our analysis suggests that treatment prescriptions based only on density

(or basal area) without diameter specifications may be insufficient from a fuels

management perspective. Further efforts to increase small diameter wood utilization are needed.

Though our study sites were limited to ecosystems where historic fires were

probably fairly frequent (table 2), our synthesis suggests that historic fire regimes may be an important consideration in fuel treatment applications. Among

our study sites, fuel treatments were most effective in those ecosystems where

fires were historically most frequent. This result might be expected, since these

are the ecosystems where fuel hazard has likely increased the most in the 20th

Century (Martinson and Omi 2002). Fuel treatment efficacy in ecosystems

where fires were historically less frequent than at our study sites is questionable and remains to be investigated.

The insignificance of treatment type and age as predictors of effectiveness is

surprising but primarily indicates a need for additional studies. Particularly

scarce is information for treatments more than 5 years old. Currently, variability is too great to distinguish the relative effectiveness of treating surface fuels

(e.g., broadcast burning) or canopy fuels (e.g., mechanical thinning) versus

combining treatments, but results from individual sites suggest that the safest

bet is to treat fuel profiles in their entirety.

For example, little difference in crown fuel conditions was found between

treated and untreated areas of the Hi Meadow fire, despite a significant treatment effect on fire severity (Omi and Martinson 2002). Though we were

unable to assess pre-fire surface fuel conditions, presumably the treatments

sufficiently modified surface fuels to reduce wildfire intensity and effects.

In contrast, thinning treatments in the Cerro Grande fire were equally effective in reducing wildfire severity regardless of whether or not the slash was

disposed. We speculate that under the extremely windy conditions during this

Performance of Fuel Treatments Subjected to Wildfires Martinson and Omi

Table 3—Variation in fuel treatment effect sizes explained by various

study site characteristics.

Explanatory variable P-value R 2 a R 2

MA

 b

Treatment type 0.45 0.18 0

Treatment age 0.50 0.13 0

Density difference 0.17 0.24 0.20

Diameter difference <0.001 0.71 1.0

Historic fire frequency 0.08 0.34 0.41

a R 2 indicates the amount of variation in mean effect sizes explained by the

explanatory variable, but ignores sampling error. b

 R 2

MA indicates the amount of reduction in the random variance component

(i.e., variation not explained by sampling error) after inclusion of the

explanatory variable.
12 USDA Forest Service Proceedings RMRS-P-29. 2003.

Martinson and Omi Performance of Fuel Treatments Subjected to Wildfires

fire, surface fuels may have had less influence on fire behavior than canopy

fuels. Explicit inclusion of weather variables as predictors of fuel treatment

effectiveness will be explored in future analyses.

Conclusions

The 20th Century has demonstrated the futility of attempts to eliminate fire

from natural landscapes. Society must learn to live with fire and the détente

will be realized most appropriately through the medium of fuel treatments.

Fuel treatments provide options for landscape management that balance societal preferences with the unavoidable recurrence of wildland fires.

Where fire threatens societal values, fuel treatments can facilitate suppression by providing safe access and egress for firefighters, as well as possible

counter-firing opportunities. In wildlands managed to include natural processes, fuel treatments may help restore fire to its historic regime, either by

restoring fuel profiles that facilitate safe management ignitions or by buffering

the border between values-at-risk and extensively managed areas where natural ignitions are allowed to play themselves out. Results from this synthesis

suggest that historic fire regimes are an important consideration in fuel treatment placement and treatments may be most effective when they complement

the objectives of ecological restoration.

Acknowledgments

The Joint Fire Science Program, McIntire-Stennis Cooperative Forestry

Funds, and the Intermountain Fire Sciences Laboratory provided funding for

this synthesis and the individual studies upon which it is based. The manuscript was improved by the thoughtful comments of S. Gallup and an

anonymous reviewer.

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USDA Forest Service Proceedings RMRS-P-29. 2003. 15

Prescribed Burning and Wildfire Risk in the

1998 Fire Season in Florida

John M. Pye1, Jeffrey P. Prestemon1, David T. Butry1,

and Karen Lee Abt1

Abstract—Measures of understory burning activity in and around FIA plots in northeastern Florida were not significantly associated with reduced burning probability in

the extreme fire season of 1998. In this unusual year, burn probability was greatest

on ordinarily wetter sites, especially baldcypress stands, and positively associated

with understory vegetation. Moderate amounts of lightning also were associated with

greater burning probability. Factors associated with reduced burn probability included road density and nearby requests for site preparation or seed tree burns,

perhaps a proxy for other intensive forest management practices. Alternative tactics

may prove more effective than fuel reduction in extreme years.

Introduction

While La Niña has previously been associated with dry and fire-prone

conditions in Florida (Brenner 1991, Brenner and Barnett 1992),

the extremely rapid transition from the Super El Niño of 1997-1998 to La

Niña in spring of 1998 brought a transition from heavy rains to extremely dry

conditions. Dry conditions were especially severe in the St. Johns River Water

Management District (SJRWMD) of northeastern Florida. Based on wildfire

records from Florida’s Division of Forestry (Jim Brenner, Florida Division of

Forestry, personal communication), wildfires typically burn about 0.7% of the

northeastern Florida landscape per year, but in 1998 they consumed as much

area as in the previous 12 years combined (figure 1).

Those records also highlight the unusual importance of lightning as an

ignition source during this period, accounting for 89% of acres burned. In 15

of the past 21 years, the incendiary/arson category has accounted for the

largest share of ignitions in this populous state. While this combination of

lightning and drought was unusual, it would be foolish to count on it never

recurring, sparking debate over whether policies that promote increased prescribed burning would be a prudent means to reduce damages should such

severe fire conditions recur. The study reported here seeks to inform that

debate by testing whether past prescribed burning as implemented in the years

prior to 1998 significantly reduced the area burned during that severe six

week fire season in northeastern Florida, when taking into account vegetation

type, vertical structure, and fragmentation, plus variables related to ignition

sources and accessibility.

To empirically test this hypothesis, we develop in this paper a model of the

probability of wildfire as a function of both on-site and neighborhood conditions. The resulting statistical tests should help identify strategies and tactics

to prevent or minimize damages from fires in future extreme drought conditions in northeastern Florida.

1Southern Research Station, USDA

Forest Service, Research Triangle Park,

NC.
16 USDA Forest Service Proceedings RMRS-P-29. 2003.

We assume that stand wildfire risk is related to both on-site and neighborhood vegetation and management conditions, weather/climate, and human

factors. Influential site conditions include fuel types and strata, soil moisture

content, stand management including prescribed burning, as well as previous

wildfires in the stand (which may be considered as a proxy for current aggregate fuel loads). Neighborhood lands can affect wildfire risk through conditions on those lands and by contagion (Chou 1993). Weather affects site wildfire risk through precipitation, evaporation, and wind and by providing a direct ignition source (lightning). Humans affect wildfire risks by (1) development patterns that alter vegetation characteristics and contiguity, thereby affecting wildfire spread and sparking; (2) providing ignition sources, including

arson and accidents; (3) suppressing fires once they have begun; and (4) managing fuels and lowering spread rates through vegetation management and

building fire breaks.

The SJRWMD includes large areas of actively managed forests, often involving the use of prescribed understory fire to control vegetation and reduce

fire hazard, as well as intentional burns to prepare harvested sites for planting

or seeding. Recent research suggests that the spatial pattern of these previous

burns or treatments may be an important factor in the spread of wildfire (Agee

and others 2000, Finney 2001) but these conclusions are based on simulations of fire and management. McKelvey and Busse (1996) had good success

stratifying areas at risk based on elevation, slope, and aspect in California’s

Sierra Nevadas. Nonetheless, they found that some areas reburned more often

than expected by chance, notably in areas adjacent to major roadways. As with

another Western U.S. analysis (Hyderdahl, Brubaker and Agee 2001), they

found statistical relationships between site characteristics and the probability

of burning but included little vegetation and no management information in

their estimates, and their most important variables of elevation, slope, and

aspect are of little relevance in the flat coastal plain of Florida.

In Mississippi, geographically more similar to Florida, Munn, Zhai, and

Evans (2003) found that slope was not an important predictor of wildfire.

Figure 1—Aggregate historical

wildfire risks in Florida 1981–2001.

Pye, Prestemon, Butry, and Abt Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida
USDA Forest Service Proceedings RMRS-P-29. 2003. 17

However, they found that wildfire occurred more often in pine and oak-pine

stands than hardwoods, and that wildfire was positively associated with proximity to development. Others have also found human presence to be positively associated with wildfire, increasing the number of ignitions and the number of large fires (Cardille and Ventura 2001; Cardille, Ventura, and Turner

2001). However, Sapsis and others (1996) found that human presence decreased the risk of large fires.

 Two studies that may be of particular importance in evaluating fire risk in

Florida address riparian areas (Fites-Kaufman 1997) and fuel connectivity

(Miller and Urban 2000), although both of these studies evaluated forest fires

in the Western U.S. Fites-Kaufman found that riparian areas had an average

fire return interval of greater than 20 years, with irregular intervals between

fires. Miller and Urban, examining Sierra Nevada forests, found that connectivity in fuels led to increased spread potential. They note, however, that connectivity is likely a minor influence when temperature and fuels are conducive

to large fire development.

Other research has focused on identifying the influence of weather and

climate, including drought, precipitation, temperature, humidity, and wind.

One study (Heyerdahl, Brubaker, and Agee 2001) found that temporal, rather

than spatial, climatic variation was the driving force in fires in Oregon. Wind

speed was not found significant in predicting large fire development (Potter

1996), though high temperatures and low humidity did contribute to large

fires. McKelvey and Busse (1996) found that all of the extreme fire years in

the Sierra Nevada occurred during hot, dry seasons, but that not all hot, dry

seasons were extreme fire years. The fit from other weather variables was weak.

Model of Wildfire Risk

In light of the previous work on wildfire risk, we specified our model of

wildfire risk in this catastrophic season for stand i in a population of I stands in

year t, Ri,t, as:

( , , , ) i,t i,t Ni ,t Li ,t Hi ,t R = f S [1]

where Si,t and Ni,t are on-site and neighborhood risk factors, respectively. The

Li,t are influences of lightning, and the Hi,t are human factors affecting risk in

that period. In a particular year, the realization of the risk for stand i is either

0 or 1, so that the occurrence of a wildfire in the stand, Wi,t, is a binary variable, whose value is influenced by functional (F) relationships between wildfire and influential factors (xi,t). Given data on these factors, an empirical representation of this model can be estimated as a binary logit (Greene 1990):

PW x

x

x

x i,t i,t

i,t

i,t

i,t ( ) exp( )

exp( ) = = ( )

¢

+ ¢

1 = ¢

1

b

b

A b [2]

Ó

Ì

Ï = otherwise

if burned in year t Wi t

0

1 , [3]

The equation was estimated using quasi-maximum likelihood covariances

and standard errors, robust to varying underlying distributions of the dependent variable. Calculations were performed using EViews (Quantitative

Microsoftware 1997).

Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida Pye, Prestemon, Butry, and Abt
18 USDA Forest Service Proceedings RMRS-P-29. 2003.

Data

The unit of analysis used in this study was individual Forest Inventory and

Analysis (FIA) forested plots in the SJRWMD (figure 2). Plot locations and

stand conditions (Si,t) were obtained from the FIA records (USDA Forest

Service Southern Research Station, Knoxville, TN).

Figure 2—Forested FIA plot

locations in the Saint Johns River

Water Management District, Florida.

Black areas indicate federal lands.

Stand/On-Site Factors

The FIA plot locations in Florida were visited by FIA crews in 1985-86 and

reported in the 1987 FIA survey, and visited again in late 1993-1994 and

reported in the 1995 FIA survey. From the FIA data, observations of plot

conditions for 1993-1994 and observations on activities occurring on the plot

between the 1987 and 1995 surveys were used in the model.

FIA field crews reported evidence of wildfire on the plot since the previous

survey. They also report evidence of prescribed burning, defined as “the occurrence of fire (excluding wildfire) not used as a site preparation tool.” For

our analysis, both the wildfire and prescribed burn variables were coded as 1 if

reported to have occurred and zero if not. The FIA surveys also reported a

measure of forest-nonforest edge as observed at the perimeter of a 20.2 ha

(50 acre) circle. This variable ranged from 0 to 9, with 0 indicating no forest

edge and 9 indexing considerable forest edge.

Stands were classified by forest type as (1) cypress [Taxodium distichum

(L.) Rich.], (2) pine [Pinus species], (3) oak-pine, and (4) hardwood types.

FIA reports five measures of vegetation strata: counts of the number of

trees in three diameter classes per 0.4 ha of forest in the stand (2.5-5 cm,

5-12.7 cm, 12.8 cm dbh and larger), plus measures of the percentage of space

occupied by non-tree vegetation at 0-0.90 m and 0.9-2.44 m above the forest

floor. These measures are intercorrelated and thus suitable for recoding into a

Pye, Prestemon, Butry, and Abt Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida
USDA Forest Service Proceedings RMRS-P-29. 2003. 19

smaller number of independent variables, which we accomplished using principal components analysis. This procedure produced two orthogonal measures that together explained the majority of variation in the five FIA variables

as measured on the different plots. The first measure, referred to as ladder fuel

index 1, most strongly reflected the two non-tree vegetation variables. The

second measure, ladder fuel index 2, reflected variations in the numbers of

small and medium trees. In each measure, higher numbers indicate more vegetation on site.

We identified burn status for each plot (Ws ) by overlaying a GIS coverage

of approximate FIA plot locations (figure 2) with a coverage of polygons representing areas burned in the SJRWMD between June 3 and July 7, 1998

(figure 3) (Barbra Sapp, St. Johns River Water Management District, personal

communication).

Figure 3—Wildfires in the 1998

wildfire season in the St. Johns River

Water Management District.

Neighborhood Factors

To ensure the integrity of the survey process, the locations of plots provided by FIA have been limited to hundredths of a degree. In this region, this

corresponds to an accuracy of 1.5 km north-to-south and 1.3 kilometers eastto-west. Unless otherwise noted, this location uncertainty defines the neighborhood size for the following neighborhood variables in this analysis.

Information on wildfire and prescribed burning history was obtained from

the Florida Division of Forestry’s individual wildfire records, running from

1986 to 1997 in our analysis, and permits for silvicultural burns, which stretched

from 1996 to 1998 in our analysis. We chose to focus on the most common

types of silvicultural burns: hazard reduction, which we equate with understory burns, and an aggregate of the site preparation and seed preparation

burn categories (“regeneration burns”). We omitted wildlife and ecological

Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida Pye, Prestemon, Butry, and Abt
20 USDA Forest Service Proceedings RMRS-P-29. 2003.

burns because of their limited use and rangeland burns because they could not

be distinguished from burns on croplands. In this paper we refer to both

FIA’s prescribed burning and Florida’s hazard reduction burns as understory

burns to avoid confusion with prescribed regeneration burns.

The location of wildfire ignitions and permits for both forms of prescribed

burns are reported by Public Land Survey (PLS) section. The wildfire data

and permit data were joined to a coverage of PLS sections (David Kelly, Florida

Division of Forestry, personal communication) for neighborhood analyses.

Wildfire, understory burn, and regeneration burn areas were all expressed

relative to forest area in the neighborhood of the FIA plot. These measures

were the ratio of the sum of the area of the wildfire or permits issued to the

area of forest in a rectangle 1.3 km (east-west) by 1.5 km (north-south) centered around the nominal FIA plot location.

For wildfire, two temporal aggregates were generated: 4 to 12 years (1986-

1994), and 1 to 3 years (1995-1997) previous to 1998. These temporal aggregates roughly correspond with the FIA survey cycle and the period between the end of that cycle and the study year. Because the plots with neighborhood regeneration burns in 1997 and 1998 experienced no burning in

1998, only the regeneration burning for 1996 was used in the model.

Two measures of forest surrounding the FIA plots were generated based on

Multiple Resource Land Cover data (Riitters 1997). These report the total

area of forestland, and the proportion of that forest classified as “woody wetlands” as opposed to “upland forest.” Small amounts of forest in the neighborhood provide one indication of fuel fragmentation, along with the FIA

measure of forest-nonforest edge.

For information on lightning we used a dataset purchased from WeatherBank,

Inc. (Edmond, OK). Originally collected through the National Lightning

Detection Network, the dataset contains records of all individual cloud-toground strikes covering northern Florida between June 3 and July 7, the most

intense period of wildfire activity. Each record reports the location of strike.

Converting these into a GIS coverage (figure 4) enabled us to calculate the

Figure 4—Lightning groundstrikes in

northeastern Florida during the

subject period, June 3–July 7, 1998.

Pye, Prestemon, Butry, and Abt Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida
USDA Forest Service Proceedings RMRS-P-29. 2003. 21

number of lightning strikes that occurred within 0.833 km of each nominal

FIA plot location (1/2 mile).

Road density information was derived from a vector coverage of paved roads

(ESRI, Inc., Redmond, CA). The coverage was rasterized to 100 m pixels and

the density of pixels containing roads was calculated in the vicinity of each FIA

plot using the 1.5 x 1.3 km neighborhood. Population density was calculated

for a similar neighborhood based on census block population numbers from

the 1990 Census, normalized by census land area.

Observations Included or Excluded

Of the 2,948 FIA plots in the SJRWMD, 46%, or 1,346 were classified as

timberland. Of these, 81 burned, and 1,255 did not. Logistic analysis requires

that factors perfectly correlated with the left-hand side variable be omitted

from the analysis. Three factors in this model showed perfect correlations.

None of the plots with the following characteristics were judged to have burned

in 1998: (1) all plots with wildfire recorded between surveys (23 plots), (2) all

plots with neighborhood regeneration burning in 1997 and 1998 (210 plots),

and (3) all plots classed as xeric (280 plots). These plots were thus excluded

from the logistic regressions. Also excluded were plots with missing data. Florida

State wildfire records do not consistently report wildfires on federal lands,

thus wildfire history data was unavailable for the 402 plots either in or near

federal lands. Also excluded were 13 plots that lacked non-tree vegetation

data. Some of the plots are in more than one of the above classes, resulting in

a total of 555 usable observations, 59 of which were burned in 1998.

Table 1 shows the mean values for the various independent variables broken out by forest type. It shows, for example, that the 52 baldcypress stands in

the sample predominantly occurred on hydric sites, had less non-tree vegetation than other types on average (ladder fuel 1) but more small trees (ladder

fuel 2), were surrounded by more wetland forest, experienced virtually no

prescribed burning on site, and had few roads and human residents nearby.

Table 1—Mean values and number of observations for FIA plots used in the logistic model, by forest type.

Forest type

Variable Units Pine Oakpine Baldcypress Hardwood

Hydric site proportion on hydric sites 0.0 0.4 0.9 0.4

Ladder fuel 1 index of ladder fuel 1 -9 -45 -95 -25

Ladder fuel 2 index of ladder fuel 2 203 341 429 248

Upland forest acres/(1.5x1.3 km) 107 89 84 79

Wetland forest acres/(1.5x1.3 km) 47 52 70 60

Rx burn 1 if prescribe burn, 0 if not 0.1721 0.0217 0.0000 0.0000

Forest edge 1 if little forest edge, 9 if a great deal 3.7 3.8 3.5 3.3

Under.burn96 proportion of forest area 0.0077 0.0164 0.0065 0.0065

Under.burn97 proportion of forest area 0.0001 0.0000 0.0001 0.0001

Under.burn98 proportion of forest area 0.0063 0.0072 0.0211 0.0044

Regen.burn96 proportion of forest area 0.0012 0.0001 0.0019 0.0056

Road density proportion of pixels containing a road 0.152 0.159 0.087 0.147

Pop. density persons per acre 0.089 0.120 0.051 0.161

Prev.wildfire1 proportion of forest area 0.0028 0.0013 0.0071 0.0029

Prev.wildfire2 proportion of forest area 0.0047 0.0061 0.0034 0.0031

Lightning ground strikes/0.25 mi2 3.23 2.87 2.02 2.74

Count number of observations 308 46 52 149

Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida Pye, Prestemon, Butry, and Abt
22 USDA Forest Service Proceedings RMRS-P-29. 2003.

Results

The results of the logistic model estimation of the probability of wildfire

are shown in table 2. Overall performance of the models was good, with the

chi-squared value significant for the model with variables as compared to the

model with only a constant. McFadden’s R-squared is 0.20, although interpretation of values between 0 and 1 are difficult with this measure (Greene

1993).

In this multivariate model, which regresses the occurrence of wildfire in

1998 on site and neighborhood variables, stand forest type is mildly predictive

of burn probability, with baldcypress stands more at risk than other forest

types. Baldcypress stands are typically associated with hydric drainage conditions, and at the other extreme from the perfectly and negatively correlated

xeric condition.

Pine stands were no more likely to burn than the intercept hardwoods in

this unusually severe drought. This may be due in part to the influence of the

two ladder fuel measures, which were highly significant and positively correlated with burning in 1998.

While non-tree and small tree vegetation were positive correlates with burning, and previous evidence of wildfire in 1994 was a perfect negative correlate,

evidence of understory burning on the site did not exert a negative influence

on burning in 1998.

As with the on-site measure of understory burning, none of the three neighborhood measures of understory burning permits had any significant negative

influence on wildfire probability in 1998. The only significant burn permit

Table 2—Logit model estimates of wildfire occurrence as a function of site, neighborhood,

lightning, and human variables (St. Johns River Water Management District, 1998, Forest

Inventory and Analysis plots). a

Variable Coefficient Standard error P value

Intercept -5.03 1.10 <0.0001

Stand/on-site

 Pine forest 0.12 0.49 0.8109

 Oak-pine forest -0.33 0.74 0.6557

 Cypress forest 1.33 0.57 0.0213

 Hydric site 0.48 0.48 0.3246

 Rx Burn (stand) 0.69 0.44 0.1168

 Ladder fuel-1 0.009 0.003 0.0048

 Ladder fuel-2 0.002 0.001 0.0084

 Forest edge 0.11 0.10 0.2736

Neighborhood

 Total timberland 0.02 0.01 0.0001

 Proportion wetland forest -3.12 0.66 <0.0001

 Understory burn-96 0.86 2.83 0.7624

 Understory burn-97 -327.14 785.36 0.6770

 Understory burn-98 1.95 3.12 0.5321

 Regeneration burn-96 -124.49 52.97 0.0188

 Previous wildfire (1995-1997) -2.34 7.36 0.7511

 Previous wildfire (1986-1994) 19.24 9.69 0.0472

 Lightning strikes (1998) 0.16 0.13 0.2251

 Ltng. strikes*ltng strikes -0.02 0.01 0.0487

 Population density -0.41 2.68 0.8774

 Road density -6.09 1.70 0.0003

a McFadden’s R-squared: 0.20. Log likelihood: -149.59. Model significance level: <.0001.

Pye, Prestemon, Butry, and Abt Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida
USDA Forest Service Proceedings RMRS-P-29. 2003. 23

measure was regeneration burning in 1996, which had a strongly significant

negative effect on the probability of fire in 1998. This is consistent with the

perfect correlation and exclusion of the plots with regeneration burning in the

neighborhood in 1997 and 1998. Regeneration burning appears to have a

negative effect on fire risk. While it is plausible that harvesting a stand and

then burning the remaining slash and vegetation would at least temporarily

reduce fuels and wildfire risk, it is also likely that some of the influence of this

measure arises from other management practices associated with intensive forestry. These may include stocking control, herbicide use, and fire breaks, none

of which are directly reflected in this model.

The model includes the neighborhood measures of historical wildfire for

1995-1997 and 1986-1994. The existence of wildfires in the last 3 years is not

significantly related to the probability of a plot burning, but wildfires in the

previous 9 years are significant and positive. This implies that areas that had

experienced wildfires more than 3 years ago were again at higher risk of fire in

1998. The exclusion of all plots with recorded on-site wildfires does not allow

direct statistical comparison, but the fact that none of these sites burned in

1998 is at least suggestive of a local and countervailing negative effect of

previous wildfires.

The fragmentation measure of forest-nonforest edge was not significant,

but the amount of forest surrounding an FIA plot was significant, with more

forest associated with increased probability of burning in 1998. However,

upland forests increased burn probability more than wetland forests, as reflected in the significant and negative effect of the proportion of wetlands.

This is in seeming contrast to the elevated burn probability for baldcypress.

One possibility is that the baldcypress stands most likely to burn are those at

the drier, more upland margins.

Of the two measures of human influence—population density and road

density—only road density is significant, and it shows a negative influence on

burn probability. As mentioned in the Introduction, the literature on the influence of human presence is inconsistent, but the differences in results may

be related to the dominant ignition sources in the dataset being examined. In

most years in northeastern Florida, accidents and arson—sources logically associated with roads and people—dominate natural ignition sources. In the

1998 wildfire season, human-caused ignitions played a minor role, allowing

influences of roads on detection and suppression to show increased importance. These potential influences include quicker detection, easier access for

suppression resources, and greater fragmentation of fuels, each of which could

result in less area burned.

Because severe drought conditions spanned the entire SJRWMD in the

spring of 1998, we do not attempt to include site-specific weather data. However, because 1998 was highly unusual in the number of lightning caused fires

and acres, we included contemporaneous lightning strikes. Studies in the Southwest and Florida (Gosz and others 1995, Shih 1988), have each shown that,

in general, lightning is strongly correlated with rainfall, and yet anecdotally

we understand that dry lightning was an important ignition source during this

period. We attempted to isolate this influence with linear and second order

terms. Our results show that lightning has an increasing then decreasing correlation with increased fire risk, with a maximum positive influence reached at

approximately eight strikes per square mile. Taken together they suggest that

small and intermediate amounts of lightning, perhaps associated with little

precipitation, can raise the probability of fire, while high levels of lightning are

correlated with suppressive rain events.

Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida Pye, Prestemon, Butry, and Abt
24 USDA Forest Service Proceedings RMRS-P-29. 2003.

Discussion

Taken together, the results indicate that in the extreme 1998 fire season in

northeastern Florida, it was forests ordinarily thought of as wet that were

most likely to burn—forests on mesic and especially hydric sites, and baldcypress

stands in particular. These locations do not match the general pattern of wildfire indicated in the recent FIA survey nor our casual expectations of areas at

risk to wildfire. However, baldcypress is not immune to wildfire. At least one

source reports that cypress ponds in north Florida typically burn several times

a century (Myers and Ewel 1990). It is noteworthy that in this drought, fire

risk areas did not merely expand outward from xeric into mesic locations on

the landscape. Fire risk simultaneously moved into hydric sites and out of the

more typically fire-prone xeric sites, changing rather than augmenting the

areas at risk.

Given the above, it perhaps should not be surprising that understory burning was not found to be a significant reducer of risk in this catastrophic year.

Xeric sites, even in areas with no previous understory burning in the area,

were apparently at low risk during this extreme period. Instead, it was

baldcypress stands that were at greatest risk. Based on the FIA data from 1994,

baldcypress stands typically have high densities of small trees but little nontree

vegetation in the lower strata. However, conditions when FIA crews visited in

1994 may have differed from those during this extreme drought, when areas

ordinarily flooded can dry sufficiently to allow understory fuels to first proliferate and then dry out. Baldcypress on the drier margins of wetland forests

might be especially prone to such ephemeral conditions, consistent with the

negative correlation with percent wetland forest (but see Myers and Ewel 1990

for a contrary fire pattern). Should this be true, understory vegetation on

hydric sites would be minimal in more ordinary years, thus precluding use of

fuel reduction treatments, whether through fire, chemical, or mechanical alternatives.

While our results do not support the hypothesis that understory burning

affects fire risk in extreme drought years, we did not examine whether controlled burns might reduce wildfire intensity or severity. Such activity could

reduce damage to the stand, and wildfires in those areas where it is practiced

may be safer to control. Understory burns may also reduce wildfire risk in

years with more typical rainfall patterns or for shorter periods of time than

tested here.

Management Implications

Our motivation for this study was to identify strategies that would mitigate

risk during future catastrophic droughts. However, prescribed understory burns

apparently do not help, at least as they have previously been conducted. While

some potential may exist to increase the protective effects of such burns through

better identification of areas of greatest benefit and spatial arrangement, the

feasibility of alternative fuel reduction methods in the important baldcypress

forests appears discouraging. This suggests we must look at other tactics beside fuel management to mitigate risk on hydric sites during extreme drought

conditions. Possible tactics could include constructing and maintaining firebreaks and ensuring defensible spaces around buildings and other areas of

value. Suppression capabilities are also important, but given the rare occurrence

Pye, Prestemon, Butry, and Abt Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida
USDA Forest Service Proceedings RMRS-P-29. 2003. 25

of these extreme drought conditions, emphasis should be given to maximizing access to suppression resources that are easily mobilized, including unused

aircraft and field crews from distant regions.

Acknowledgments

We would like to thank the following individuals for their careful review

and helpful comments on an earlier version of this paper: Robert J. Huggett,

Jr., Ian Munn, Jim Brenner, Susan Howell, Rudy King, and Reviewer #2.

Literature Cited

Agee, J.K., B. Bahro, M.A. Finney, P.N. Omi, D.B. Sapsis, C.N. Skinner, J.W. van

Wagtendonk, and C.P. Weatherspoon. 2000. The use of fuelbreaks in landscape

fire management. Forest Ecology and Management 127(1-3):55-66.

Barnett, T. P., and Brenner, J. 1992. Prediction of wildfire activity in the southeastern

United States. Southeast Regional Climate Center Research Paper No. 011592.

South Carolina Water Resources Commission. Columbia, SC.

Brenner, J. 1991. Southern oscillation anomalies and their relation to Florida wildfires.

Fire Management Notes 52(1):28-32.

Cardille, J.A., S.J. Ventura. 2001. Occurrence of wildfire in the northern Great Lakes

Region: effects of land cover and land ownership assessed at multiple scales.

International Journal of Wildland Fire 10(2):145-154.

Cardille, J.A., S.J. Ventura, and M.G. Turner. 2001. Environmental and social factors

influencing wildfires in the Upper Midwest, United States. Ecological Applications

11(1):111-127.

Chou, Y.H., R.A. Minnich, and R.A. Chase. 1993. Mapping probability of fire

occurrence in the San Jacinto Mountains, California, USA. Environmental

Management 17(1):129-140.

Finney, M.A. 2001. Design of regular landscape fuel treatment patterns for modifying

fire growth and behavior. Forest Science 47(2):219-228.

Fites-Kaufman, J. 1997. Historic landscape pattern and process: Fire, vegetation, and

environment interactions in the northern Sierra Nevada. Ph.D. dissertation. Seattle,

WA: University of Washington.

Gosz, J.R., D.I. Moore, G.A. Shore, H.D. Grover, W. Rison, and C. Rison. 1995.

Lightning estimates of precipitation location and quantity on the Sevilleta LTER,

New Mexico. Ecological Applications 5(4):1141-1150.

Greene, W. H. 1993. Econometric analysis. MacMillan, New York, NY. 783 p.

Heyerdahl, E.K., L.B. Brubaker, J.K. Agee. 2001. Spatial controls of historical fire

regimes: a multiscale example from the Interior West, USA. Ecology 82(3):

660-678.

McKelvey, K.S. and K.K. Busse. 1996. Twentieth-century fire patterns on Forest

Service lands. In Sierra Nevada Ecosystem Project: Final Report to Congress, Vol.

II, Chap. 44. Davis: University of California, Centers for Water and Wildland

Resources.

Miller, C. and D.L. Urban. 2000. Connectivity of forest fuels and surface fire regimes.

Landscape Ecology 15(2):145-154.

Munn, I.A., Y. Zhai, and D.L. Evans. 2003. Modeling forest fire probabilities in the

South Central United States using FIA data. Southern Journal of Applied Forestry

27(1):11-17.

Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida Pye, Prestemon, Butry, and Abt
26 USDA Forest Service Proceedings RMRS-P-29. 2003.

Myers, Ronald L. and John J. Ewel. 1990. Ecosystems of Florida. Orlando: University

of Central Florida Press.

Potter, B.E. 1996. Atmospheric properties associated with large wildfires. International

Journal of Wildland Fire 6(2):71-76.

Sapsis, D.B., B. Bahro, J. Spero, J. Gabriel, R. Jones, and G. Greenwood. 1996. An

assessment of current risks, fuels, and potential fire behavior in the Sierra Nevada.

Sierra Nevada Ecosystem Project. Final Report to Congress, Vol. III. Davis:

University of California, Centers for Water and Wildland Resources.

Shih, S.F. 1988. Using lightning for rainfall estimation in Florida. Transactions of the

ASAE, American Society of Agricultural Engineers 31(3):750-755.

Riitters, K.H., R.V. O’Neil and K.S. Jones. 1997. Assessing habitat suitability at

multiple scales: a landscape-level approach. Biological Conservation 81:191-202.

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Pye, Prestemon, Butry, and Abt Prescribed Burning and Wildfire Risk in the 1998 Fire Season in Florida
USDA Forest Service Proceedings RMRS-P-29. 2003. 27

Fire Hazard and Silvicultural Systems:

25 Years of Experience From the Sierra Nevada

Scott Stephens1 and Jason Moghaddas1

Silviculture systems influence fire hazard by changing the arrangement

and quantities of live and dead biomass. Each system regulates forest

growth and development but their long-term influence on fire hazard is largely

unknown. We determined if significant differences (p < 0.05) exist in fuel

loads (1, 10, 100, 1000 hour, duff, litter), surface fuel height, crown cover,

and height to live crown base from the single-tree selection, thin from below,

overstory removal, group selection, and clear-cut silvicultural systems. Approximately 25 years of data from each of the systems were analyzed from

young-growth (approximately 80 years old) mixed conifer forests at the University of California Blodgett Research Station located in the northern Sierra

Nevada. These systems were also compared to adjacent 80-year-old unmanaged

stands. All areas have experienced a policy of fire suppression the last century.

Activity fuels were lopped and scattered with the exception of the clear-cut

and group regeneration units that were tractor piled and burned. Significant

differences were detected between silvicultural systems with the clear-cut having the lowest average fuel loads. Clear-cut had low surface loads but at 10-15

years of age they produced high horizontal fuel continuity and high hazards.

No significant differences were detected between the single tree selection,

overstory removal, thin from below, and unmanaged stands, which all averaged approximately 150 metric tons/ha of surface and ground fuels. Activity

fuel treatment must be an integral component of silvicultural systems to produce forests with low fire hazards.

1Division of Forest Science, Department

of Environmental Science, Policy, and

Management, University of California,

Berkeley.
28 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 29

Canopy Fuel Treatment Standards for the

Wildland-Urban Interface

Joe H. Scott1

Abstract—This paper describes a canopy fuel treatment standard based on models

of crown fire flame size, initiation, spread rate, and firefighter safety. Site-specific

prescriptions can be developed with NEXUS or nomograms. A general prescription

designed to be effective at 20-ft windspeeds up to 25 mph during drought summer

fine dead fuel moisture conditions (1-hr = 4%, 10-hr = 5%, 100-hr = 7%, live =

78%) calls for a crown-fire-free zone (CFFZ) 380 ft wide with a maximum canopy

bulk density of 0.10 kg/m3. Minimum canopy base height ranges from 2 to 18 ft

depending on surface fuel conditions; for fuel model 10 (timber litter and understory), minimum canopy base height is 13 ft.

Introduction

Houses and other structures can be ignited during a wildland fire by

direct flame contact, radiation, or burning embers. The probability of

structure ignition can be greatly reduced, but not eliminated, by surface fuel

modification immediately adjacent to structures and by adherence to design

and construction standards for the structure itself. However, except for an

exceptionally well-designed structure, firefighter intervention is needed during the passage of a wildland fire to suppress incipient ignitions. Therefore,

when designing fuel treatments for structure protection in the wildland-urban

interface we should plan for the presence of firefighters at a structure during

fire front passage.

Firefighters need a zone around the structure in which to lay hose, raise

ladders to the roof, inspect the home exterior for ignitions, and suppress external structure ignitions. This immediate area around the structure should

not allow a spreading surface fire. Surface fuels around this fire-free zone must

be treated so that flame lengths allow firefighters to work safely.

Even in full protective wildland clothing, firefighters are more prone to

burn injury from flames than a structure is prone to ignition by radiation

(Cohen and Butler 1998). In other words, radiation from flames will injure a

firefighter or homeowner before untreated wood siding would ignite. Therefore, fuel treatments around structures should be designed to protect

firefighters, not structures. Fuels should be treated such that the structure is

within a firefighter safety zone. This is the basis for defensible space—the area

around a structure where firefighters can safely work (California State Board

of Forestry 1996). Many surface fuel treatment standards for creating defensible space exist (for example: International Fire Code Institute 1997, Moore

1981). This paper presents a method for determining the size and characteristics of a coniferous forest canopy treatment.

Firefighter safety zone size, and thus the required area of canopy fuel modification around a structure, is a function of expected flame height. A physical

1Systems for Environmental

Management, Missoula, MT.
30 USDA Forest Service Proceedings RMRS-P-29. 2003.

heat transfer model suggests this distance must be about 4 times expected

flame height, not including a factor of safety (Butler and Cohen 1998). Flame

heights are always less than or equal to flame length. To be conservative and

simplify, I assume that flame length equals flame height.

Crown fires present special problems for structure protection. They generate huge quantities of burning embers, some of which may travel long distances.

Crown fires have very tall flames, which indicate need for a large safety zone

and therefore wide fuel modification distance. Modifying canopy fuels to preclude crown fire near a structure is a critical part of ensuring firefighter safety

during structure protection.

The guidelines presented here are not intended to be applied by firefighters

assigned to structure protection during an active fire—they should be implemented by structure owners before fire threatens.

The Design Environmental Condition

Before prescribing a canopy fuel treatment one must first specify the design

environmental condition—the most extreme condition under which the treatment is expected to produce the prescribed result. There are only two factors

in the design condition: fine dead fuel moisture content and 20-ft windspeed.

Fine dead fuel moisture content can be set in either of two ways. One method

is to identify a threshold condition from a local fire weather database, such as

90th, 95th, or 99th percentile. Firefamily Plus (Bradshaw and McCormick 2000)

is a good tool for such analysis. Where such data are not available, the design

fine dead fuel moisture content can simply be set to a standard set of moistures, such as those used by Rothermel (1991) in his crown fire spread model

(table 1). These values are for the northern Rocky Mountains; other regions

may need to use a different set if fuel moistures vary significantly from these.

The design windspeed is more difficult to determine from a fire weather

database. Many fire weather databases have a single daily windspeed observation, and this is often a 10-minute average. Windspeeds much higher than

reported can occur at the station during other times of the day; the 10-minute

average masks significant variability. If using windspeed data, I recommend

setting the design windspeed to a value that represents the near maximum

(95th or 99th percentile) 1-minute average windspeed that can occur at a site.

The design windspeed can alternatively be set to a reasonable value based on

expert knowledge of local conditions and the windspeed at which firefighters

would discontinue operations. Design windspeeds between 20 and 40 mph

are reasonable—I use 25 mph as a default.

Table 1—Rothermel’s (1991) fine dead fuel moisture content scenarios. Values are component

moisture content (percent).

 Timelag Early spring Late spring Late summer

 fuel before after Normal Drought severe

component greenup greenup summer summer drought

1-h 8 9 64 3

10-h 14 11 8 5 4

100-h 18 15 10 7 6

Live 65 195 117 78 70

Scott Canopy Fuel Treatment Standards for the Wildland-Urban Interface
USDA Forest Service Proceedings RMRS-P-29. 2003. 31

Regardless of how the design condition is selected, it is important that this

condition be communicated clearly to all responsible parties. It is the limit of

effectiveness of the treatment—analogous to the load limit on a bridge.

Size of the Crown-Fire-Free Zone

There are three elements of the canopy fuel treatment standard: required

size of the treated area, and within it, maximum allowable canopy bulk density, and minimum allowable canopy base height.

The first element of the canopy fuel treatment standard is the size of the

treatment area, called the crown-fire-free-zone (CFFZ). The CFFZ size is a

function of the expected flame size. For a site-specific treatment design, determine potential crown fire flame length for the design condition using

Rothermel’s (1991) nomograms or by using NEXUS (Scott 1999;

www.fire.org/nexus/nexus.html), then multiply this estimate by 4 to get the

minimum radius of the CFFZ. For designing a non-specific treatment, use the

nomogram in figure 1, which uses drought summer fuel moistures (table 1)

and requires an estimate of the total available surface and canopy heat per unit

area (HPA) in the area surrounding the CFFZ. Surface and canopy fuel HPA

have been combined into four categories encompassing the range likely to be

encountered (table 2).

At the default design windspeed of 25 mph, drought summer fine dead fuel

moisture (table 1), and high surface and canopy fuel HPA (table 2), the CFFZ

must be a minimum of 380 ft radius in all directions from the structure (figure 1).

Figure 1—Minimum width of the

crown-fire-free zone around a

structure as a function of 20-ft

windspeed for 4 categories of

combined surface and canopy fuel

heat per unit area (low = 3500 BTU/

ft2

, medium = 5000 BTU/ft2, high =

6000 BTU/ft2

, extreme = 7000 BTU/

ft2). This chart uses Rothermel’s

(1991) crown fire spread model to

estimate flame length, and assumes

level ground, drought summer fine

dead fuel moisture conditions (1-hr

= 4%, 10-hr = 5%, 100-hr = 7%,

live = 78%). Width of the crownfire-free zone is 4 times the

estimated flame length.

Canopy Fuel Treatment Standards for the Wildland-Urban Interface Scott
32 USDA Forest Service Proceedings RMRS-P-29. 2003.

Canopy Fuel Characteristics in the CFFZ

Two canopy fuel characteristics must be controlled in the CFFZ: canopy

bulk density (CBD) and canopy base height (CBH). Canopy bulk density, the

mass of available canopy fuel per unit of canopy volume, must be low enough

to cause an active crown fire to cease when entering the CFFZ. Canopy base

height must be high enough to preclude initiation of passive crown fire within

the CFFZ.

Maximum Allowable Canopy Bulk Density

Fuels within the CFFZ must be managed so that an active crown fire spreading to the zone would cease. This is accomplished by reducing canopy bulk

density (CBD) below a threshold value (figure 2). The threshold CBD is determined by linking separate models of crown fire spread rate (Rothermel

1991) and critical conditions for active crown fire spread (Van Wagner 1977).

Table 2—Stylized surface and canopy fuel loadings and heat per unit area, for estimating

crown fire flame length in Rothermel’s (1991) correlation. Drought summer fuel moisture is

assumed (see table 1) for estimating heat per unit area of surface fuels. Available canopy

fuel load can be estimated using tables in Rothermel (1991). Fire behavior fuel models

(FBFM) are described in Anderson (1982).

 Fuel

 complex Available

 heat per Additional canopy fuel Heat per

 unit area coarse fuels load unit area

class Surface fuel model (tons/ac) (tons/ac) (BTU/ft2)

Low FBFM 8

Compact timber litter 8 3500

Medium FBFM 10

Timber litter and understory 10 5000

High FBFM 10

Timber litter and understory 30 12 6000

Extreme FBFM 12

Medium logging slash 12 7700

Figure 2—Maximum allowable

canopy bulk density as a function

of 20-ft windspeed, for a range of

fine dead fuel moisture content

scenarios. Level ground is

assumed.

Scott Canopy Fuel Treatment Standards for the Wildland-Urban Interface
USDA Forest Service Proceedings RMRS-P-29. 2003. 33

It is largely a function of the design windspeed (Scott and Reinhardt 2001).

At the design condition of drought summer fuel moisture and 25 mph open

windspeeds, the maximum allowable CBD is approximately 0.10 kg/m3

.

Unfortunately, CBD is difficult to estimate and prescribe in a treatment.

CBD can be determined from indirect measures such as leaf area index, stand

biometrics, or from optical sensors (Scott and Reinhardt 2001). Currently,

the best method of estimating CBD is to use modified allometric equations

that relate tree species and size to crown biomass. The Fuels Management

Analyst Plus suite (www.fireps.com) contains the program Crown Mass, the

only tool available to help managers estimate CBD from allometric equations.

For a site-specific treatment specification that includes fine dead fuel moistures not included in table 1, the user must use NEXUS.

Minimum Allowable Canopy Base Height

To minimize the amount of individual-tree torching that occurs, the CFFZ

must be resistant to crown fire initiation. Van Wagner’s (1977) model of crown

fire initiation determines the fireline intensity necessary to initiate crowning

based on canopy base height and foliar moisture content. Foliar moisture content is not a significant factor in the model and can be held constant at 100

percent (Scott 1998b). Because fireline intensity is related to flame length

(Byram 1959), we can express the minimum allowable canopy base height as

a function of expected surface fire flame length (figure 3). The nomograms

produced by Scott and Reinhardt (2001) are useful for computing this threshold

crown base height directly from fuel model and fuel moisture condition.

NEXUS can be used for conditions not represented by the nomograms. For

generic treatment planning, use the chart in figure 4, which assumes drought

summer fuel moistures and a wind adjustment factor of 0.25 to represent the

more open conditions of a treated stand. For the design condition of fire

behavior fuel model 10, drought summer fuel moisture, and a 25 mph 20-ft

windspeed, the minimum allowable CBH is 13 ft (figure 4).

Figure 3—Minimum allowable

canopy base height as a function of

surface fire flame length, as

predicted from Van Wagner’s (1977)

crown fire initiation model and

Byram’s flame length model.

Canopy Fuel Treatment Standards for the Wildland-Urban Interface Scott
34 USDA Forest Service Proceedings RMRS-P-29. 2003.

Discussion

Previous work based on similar models suggests fuels need to be treated a

maximum of only 130 ft from a structure (Cohen and Butler 1998). However, that recommendation is based on preventing piloted ignition of wood by

radiation, not protecting firefighters or the homeowner who may be present

during fire front passage. Because structures are so susceptible to ignition by

firebrands, if exterior ignitions are to be suppressed, people may need to be

present. The treatment standard presented in this paper is designed to protect

those people.

The default maximum allowable CBD in this analysis falls within the range

of 0.074 to 0.125 kg/m3 found by Agee (1996) to cause cessation of the

crown fires reported by Rothermel (1991). Agee also found an empirical threshold of 0.10 kg/m3 on the 1994 Wenatchee fires, which coincides exactly with

the value from this analysis for drought summer fuel moisture and 25 mph

20-ft winds.

The three elements of the standard do not have an explicit factor of safety

attached. There is no built-in safety factor for the prescribed CBD or CBH. However, several factors lead to a built-in margin of safety in the treatment size.

Flame length is used in place of flame height. Models of flame angle and

flame height are now being developed, but not yet available. Using flame

length in place of height slightly overestimates the required treatment.

The radiation model assumes that the flames radiate directly onto firefighters,

but in reality any remaining trees in the CFFZ block some of the radiation before it reaches firefighters at the structure. The amount of blocking is not modeled,

but may be significant. Blocked radiation by trees leads to a margin of safety.

The treatment assumes that active crowning is possible outside the CFFZ.

If only passive crowning is possible outside the CFFZ, then flame length is

over-predicted by the method.

Existing surface fuel treatment standards suggest larger treatment distances

on steeper slopes, and generally only on the downslope side of the house. This

standard suggests treating the same distance in all directions, regardless of

slope steepness. Steeper slopes are well known to increase fireline intensity

Figure 4—Minimum allowable

canopy base height as a function

of 20-ft windspeed, for a variety

of fire behavior fuel models,

assuming level ground, wind

adjustment factor 0.25 (represents

treated stand condition), and

drought summer fine dead fuel

moisture conditions (1-hr = 4%,

10-hr = 5%, 100-hr = 7%, live =

78%).

Scott Canopy Fuel Treatment Standards for the Wildland-Urban Interface
USDA Forest Service Proceedings RMRS-P-29. 2003. 35

and spread rate, with the greatest effect in the uphill direction. However,

windspeed usually has an even greater effect on fireline intensity and spread

rate than slope steepness, but the direction of the effect is not known before

the fire. Fire-carrying winds can be from any direction. In many cases a

downslope wind would overpower the effect of slope steepness. If the direction of fire-carrying winds is well established for a site, then treatment distance

from a structure could be reduced on the downwind side.

The models used in this method suggest very wide canopy fuel treatment

areas. However, in many cases the canopy fuels are already within CBD and

CBH limits, so no treatment will be necessary. In other cases a light treatment

will reduce CBD to the desired level. In no case is complete removal of the

forest canopy required to mitigate crown fire potential near a structure.

This standard also makes a structure less prone to ignition from embers.

Ember exposure is reduced by eliminating crown fire immediately near the

structure. Firebrand research has focused on maximum spotting distance from

surface and crown fires, as fire-starting embers can travel very long distances

in a convection column. However, little is known about the distribution of

spotting distances from a crown fire or torching trees. It is likely that, while

long spotting distances are attainable, most embers capable of igniting a structure do not travel very far at all. Therefore, reducing crown fire activity in the

vicinity of a structure reduces its exposure to firebrands and thus its potential

for ignition.

The cost of modifying canopy fuels to comply with this standard will vary

widely. It depends in part on timber markets, terrain and stand conditions,

method of treatment, and type of activity fuel treatment. In comparing three

alternative treatments to reduce canopy fuels in second-growth ponderosa

pine, Scott (1998a) found positive net returns of $156 to $832 per acre treated,

depending on logging method, volume of trees removed and type of activity

fuel treatment. However, those same treatments applied in different stands

might not produce the same revenue.

Modifying canopy fuels as prescribed in this method may lead to increased

surface fire intensity and spread rate under the same environmental conditions, even if surface fuels are the same before and after canopy treatment.

Reducing CBD to preclude crown fire leads to increases in the wind adjustment factor (the proportion of 20-ft windspeed that reaches midflame height).

Also, a more open canopy may lead to lower fine dead fuel moisture content.

These factors increase surface fire intensity and spread rate. Therefore, canopy

fuel treatments reduce the potential for crown fire at the expense of slightly

increased surface fire spread rate and intensity. However, critical levels of fire

behavior (limit of manual or mechanical control) are less likely to be reached

in stands treated to withstand crown fires, as all crown fires are uncontrollable. Though surface intensity may be increased after treatment, a fire that

remains on the surface beneath a timber stand is generally controllable.

If left untreated, activity fuel created while reducing CBD can exacerbate

this increase in surface fire intensity and spread rate. Whole-tree harvesting or

pile burning or broadcast burning of activity fuel is recommended following

canopy fuel treatment to reduce surface fuel flammability.

Conclusion

Existing fuel treatment standards adequately address surface fuel only; they

are assumed to be effective in creating defensible space at a structure. This

Canopy Fuel Treatment Standards for the Wildland-Urban Interface Scott
36 USDA Forest Service Proceedings RMRS-P-29. 2003.

paper presents a simple method of designing canopy fuel treatments likely to

protect firefighters and homeowners in the wildland-urban interface.

Firefighters attempting structure protection during passage of a fire front can

work only within a safety zone. These guidelines create a crown-fire-free zone

large enough that the tall flames from an active crown fire are unlikely to

injure people protecting structures during a fire.

This method requires the manager to specify the design environmental conditions for the treatment. This design condition represents the limit of

effectiveness of the treatment and should be communicated to homeowners

and firefighters.

Acknowledgments

This research was supported in part with funds provided by the Joint Fire

Sciences Program, the USDA Forest Service Rocky Mountain Research

Station, and Systems for Environmental Management.

References

Agee, James K. 1996. The influence of forest structure on fire behavior. In: Proceedings

of the 17th Annual Forest Vegetation Management Conference; 1996 January

16–18; Redding CA: 52-68.

Anderson, Hal. E. 1982. Aids to determining fuel models for estimating fire behavior.

Gen. Tech. Rep. INT-122. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Forest and Range Experiment Station. 22 p.

Bradshaw, Larry; McCormick, Erin. 2000. FireFamily Plus user’s guide, version 2.0.

Gen. Tech. Rep. RMRS-GTR-67WWW. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Research Station.

Butler, Bret W.; Cohen, Jack D. 1998. Firefighter Safety Zones: A theoretical model

based on radiative heating. International Journal of Wildland Fire. 8(2): 73-77.

Byram, George. M. 1959. Combustion of forest fuels. In: Forest fire: Control and

use, 2nd edition. New York, NY: McGraw-Hill: 61-89.

California State Board of Forestry. 1996. California Fire Plan: a framework for

minimizing costs and losses from wildland fires. Sacramento, CA: State of California.

104 p.

Cohen, Jack. D.; Butler, Bret. W. 1998. Modeling potential structure ignitions from

flame radiation exposure with implications for wildland/urban interface fire

management. In: 13th Conference on Fire and Forest Meteorology; 1996 October

27-31; Lourne, Victoria, Australia. Proc. Fairfield, WA: International Association

of Wildland Fire: 81-86.

International Fire Code Institute. 1997. 1997 Urban-Wildland Interface Code.

Whittier, CA: International Conference of Building Officials. 47 p.

Moore, Howard E. 1981. Protecting Residences from Wildfires: a guide for

homeowners, lawmakers, and planners. Gen. Tech. Rep. PSW-50. Berkeley, CA:

U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station.

44 p.

Rothermel, Richard. C. 1991. Predicting behavior and size of crown fires in the

Northern Rocky Mountains. Res. Pap. INT-438. Ogden, UT: U.S. Department

of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

46 p.

Scott Canopy Fuel Treatment Standards for the Wildland-Urban Interface
USDA Forest Service Proceedings RMRS-P-29. 2003. 37

Scott, Joe. H. 1998a. Fuel reduction in residential and scenic forests: a comparison of

three treatments in a western Montana ponderosa pine stand. Res. Pap. RMRSRP-5. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky

Mountain Research Station. 19 p.

Scott, Joe. H. 1998b. Sensitivity analysis of a method for assessing crown fire hazard

in the Northern Rocky Mountains, USA. In: Viegas, D. X., ed.; III International

Conference on Forest Fire Research; 14th Conference on Fire and Forest

Meteorology; 1998 November 16-20; Luso, Portugal. Proc. Coimbra, Portugal:

ADAI. Volume II: 2517-2532.

Scott, Joe. H. 1999. NEXUS: a system for assessing crown fire hazard. Fire

Management Notes. 59(2): 20-24.

Scott, Joe H.; Reinhardt, Elizabeth D. 2001. Assessing crown fire potential by linking

models of surface and crown fire behavior. Res. Pap. RMRS-RP-29. Fort Collins,

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research

Station. 59 p.

Van Wagner, C. E. 1977. Conditions for the start and spread of crown fire. Canadian

Journal of Forest Research. 7: 23-34.

Canopy Fuel Treatment Standards for the Wildland-Urban Interface Scott
38 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 39

Reducing Crown Fire Hazard in Fire-Adapted

Forests of New Mexico

Carl E. Fiedler1 and Charles E. Keegan2

Abstract—Analysis of FIA data for New Mexico shows that 2.4 million acres of ponderosa pine and dry mixed conifer forests rate high for fire hazard. A restoration

treatment designed to address altered ecological conditions in these forests increased

average crowning index (i.e., the wind speed necessary to maintain a crown fire) by

50 mph, compared to only 23 mph for a thin-from-below approach designed to

reduce hazard. After we projected treated stands forward 30 years, only one-eighth

of the acres receiving the thin-from-below treatment remained low hazard, compared to over half receiving the restoration treatment.

Introduction

Recent wildfires provide harsh testimony to the hazardous forest

conditions that exist over large areas of New Mexico. The fires of

2000 are especially notable, not just in terms of acres burned, but particularly

because of the significant damage to property and associated threats to people.

There is now both the public support and political will for major initiatives to

address this regional concern (Western Governors’ Association 2001). For

example, tens of millions of dollars have been distributed through the

National Fire Plan, much of it dedicated to reducing hazardous fuels. However, planning to address fire hazard at a strategic level requires understanding

the forest conditions most vulnerable to fire and the effectiveness of alternative hazard reduction treatments.

Absence of a detailed, systematic, and uniform forest inventory for all acres

and ownerships has heretofore precluded a comprehensive analysis of fire hazard in New Mexico. However, recent availability of Forest Inventory and

Analysis (FIA) data, which are collected using consistent inventory protocols

across all ownerships, made possible this strategic assessment of fire hazard at

a statewide level.

Objectives

The goals of this study were to assess existing forest conditions and fire

hazard in New Mexico and to evaluate the potential effectiveness of hazard

reduction treatments. Specific objectives were to:

• Quantify occurrence of short-interval, fire-adapted forests in New Mexico.

• Evaluate existing forest conditions for crown fire hazard.

• Develop alternative treatment prescriptions and evaluate their effectiveness

in reducing hazard.

• Project existing and treated conditions 30 years into the future and reevaluate fire hazard.

1School of Forestry, University of

Montana, Missoula, MT.

2Bureau of Business and Economic

Research, University of Montana,

Missoula, MT.
40 USDA Forest Service Proceedings RMRS-P-29. 2003.

Methods

Forest Inventory and Analysis data were used to profile existing conditions

of forestland in New Mexico. FIA data are collected using a systematic grid,

with each inventory point or “cluster” representing approximately 6500 acres.

The FIA inventory includes forestlands in national parks and other reserved

lands, such as designated wilderness areas. The initial analysis of FIA data for

this study included lands classified as “timberlands” (i.e., primarily stocked

with traditional timber species) and “woodlands” (i.e., primarily stocked with

pinyon pine [Pinus edulis], juniper [Juniperus spp.], or hardwoods other than

aspen [Populus tremuloides] or cottonwood [Populus spp.]).

The assessment of fire hazard reported here was conducted exclusively on

short-interval, fire-adapted forests. In New Mexico, these are primarily comprised of ponderosa pine (Pinus ponderosa) and dry mixed conifer forest types.

Fire-adapted forests were identified as the highest priority for treatment in

“Protecting People and Sustaining Resources in Fire-Adapted Ecosystems —

A Cohesive Strategy” (DOI 2001). Frequent, low-intensity fires were the primary agent that shaped these forests historically and kept them resistant to

severe fires (Agee 1998). Effective fire-suppression efforts and some logging

practices over the last century have resulted in density and structural changes

that leave these forests vulnerable to severe damage from wildfire (Covington

and Moore 1994).

Fire Hazard

Potential fire hazard was analyzed for individual inventory clusters (n =

261) using the Fire and Fuels Extension (FFE: Beukema et al. 1997, Scott

and Reinhardt 2001) to the Forest Vegetation Simulator (FVS: Crookston

1990, Stage 1973, Van Dyck 2001). The FFE extension estimates crown fire

hazard based on tree, stand, and site characteristics, and expresses fire

hazard/effects in terms of crowning index, torching index, and basal area

mortality. Model runs were made assuming a 20-foot wind speed of 20 mph,

a moisture level of one (driest condition), air temperature of 90 degrees F,

and a wildfire rather than prescribed burning situation.

Crowning index, defined as the wind speed necessary for a fire that reaches

the canopy to continue as a crown fire, was the primary variable used to report

hazard in this study. Crowning index is largely driven by canopy bulk density,

which FFE calculates from individual tree biomass summed to the stand level

(Scott and Reinhardt 2001). High-hazard forest conditions were defined as

having a crowning index less than 25 mph, moderate hazard from 25 to 50

mph, and low hazard greater than 50 mph. The Central Rockies variant of the

FVS model was used to project post-treatment forest conditions 30 years into

the future, at which time crown fire hazard (i.e., crowning index) was again

assessed using FFE. Two model types were used—model type 1 (southwestern mixed conifer) and model type 2 (southwestern ponderosa pine) for the

dry mixed conifer and ponderosa pine forest types, respectively. Default site

index values were used (DF 70 for model type 1, and PP 70 for model type 2),

and regeneration was turned “on.”

Hazard Reduction Treatments

A variety of management approaches can potentially be used to address

hazardous conditions in short-interval, fire adapted forests; three contrasting

ones are compared in this paper. One approach is low thinning to a given

Fiedler and Keegan Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico
USDA Forest Service Proceedings RMRS-P-29. 2003. 41

diameter limit, a treatment that has been widely recommended (Dombeck 1997).

A diameter limit of 9 inches was used in this analysis (thin-from-below to 9 inches).

A second approach retains all trees larger than 16 inches. This prescription

(16 inches diameter-limit) is influenced by concerns that there may be a deficit of trees in the Southwest greater than 16 inches compared to historic levels,

and that cutting trees larger than this size is economically rather than ecologically motivated.

A third approach is aimed at initiating restoration of sustainable structure

and composition (and ultimately, ecological function). It therefore focuses on

the trees to leave in terms of a target density, diameter distribution, and species composition (Fiedler et al. 1999, Fiedler et al. 2001). Under this

prescription (restoration), trees are marked for leave in the sizes, numbers,

species, and juxtaposition that will go furthest toward restoring a sustainable

structure, given existing stand conditions. Most of the 40 to 50 ft2

/acre target reserve basal area is comprised of larger trees, although some trees are

marked for leave throughout the diameter distribution, if available. Silvicultural treatments involved in the restoration approach include a low thinning

to remove small trees, improvement cutting to remove late-successional species (if present), and selection cutting to reduce overall density and promote

regeneration of ponderosa pine. The multiple objectives of the restoration

treatment are to reduce fire hazard, increase tree vigor, spur development of

large trees, and induce regeneration of seral species.

A common objective of all three treatments is to reduce density (in varying

degrees) and create a discontinuity in the vertical fuel profile by cutting the

sapling- and pole-sized ladder fuels. Reducing the hazard associated with these

smaller cut trees, as well as the tops and limbs of merchantable-sized trees (if

any) that are harvested as part of the overall treatment, is an integral part of

each prescription. The resulting slash is lopped and scattered, broadcast burned,

or piled and burned depending on volume, reserve stand density, landowner

objectives, and cost considerations.

All three prescriptions were applied to high hazard conditions (i.e., those

with a crowning index <25 mph) in the ponderosa pine and dry mixed conifer

forest types. The thin-from-below to 9 inch prescription was only applied to

stands that had greater than 50 ft2

/acre of trees larger than 9 inches. For the

16 inch diameter-limit prescription, all trees larger than 16 inches dbh were

left, so long as the basal area of these trees was 50 ft2/acre or greater. If there

were less than 50 ft2/acre of basal area in trees >16 inches , then trees

<16 inches were retained from the biggest on down (i.e., 15 inches, 14 inches,

13 inches, etc.) until a basal area density of 50 ft2

/acre was reached.

The restoration prescription differed from the other two prescriptions in

that it set a target reserve density of 50 ft2

/acre in all stands designated for

treatment. Most of the basal area marked for leave was concentrated in the

larger trees, but smaller amounts of basal area were reserved in trees across the

full diameter distribution, if available.

Results

Fire Hazard: Existing Conditions

Analysis of FIA data shows that there are approximately 16.6 million acres

of woodlands/forestlands in New Mexico. The short-interval, fire-adapted

forests that are of greatest concern in terms of fire hazard collectively occupy

Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico Fiedler and Keegan
42 USDA Forest Service Proceedings RMRS-P-29. 2003.

4.0 million acres. Approximately 61%, or 2.4 million acres, of these historically fire-adapted forests classify as high hazard, 31% as moderate hazard, and

only 8% as low hazard, based on crowning index (figure 1).

The trends in crowning index across density and structural classes were

especially notable. For example, focusing on short-interval, fire-adapted forests only, average crowning index declined (i.e., hazard increased) across the

range of densities from 39 mph at low density, to 22 mph at moderate density,

to 18 mph at high density. In stands with multi-storied structures, 85% were

rated high hazard if they were also in the high-density category, whereas only

15% of moderate-density and no low-density stands with multi-storied structure received this rating.

Figure 1—Proportion of New

Mexico’s short-interval, fireadapted forests (4.0 million acres)

by fire hazard rating.

Fire Hazard: Treated Conditions

Evaluation of the effectiveness of hazard reduction treatments focused on

1.7 million acres of high hazard forests within short-interval, fire-adapted ecosystems (i.e., ponderosa pine and dry mixed conifer forest types). The average

composition of these high-hazard conditions is shown in figure 2. Approximately 0.7 million acres were not evaluated because stand composition was

such that less than 50 ft2/acre of basal area would remain following application of one or more of the treatment prescriptions. Both treatment effectiveness

and the durability of effects varied by prescription (table 1). For example,

thinning-from-below increased average crowning index from 16 to 39 mph,

or 23 mph over existing conditions, compared to increases of 45 and 50

mph for the diameter-limit and restoration treatments, respectively (table

1). The Friedman test found significant differences (p<0.001) among the

distributions of crowning indexes for the four treatments (i.e., existing conditions and three hazard reduction treatments). Multiple comparison tests

(Hochberg and Tamhane 1987) also found that all three treatments significantly increased crowning indexes (i.e., reduced crown fire hazard) relative to

untreated conditions (p=0.001). In addition, both the 16 inches diameterlimit and restoration treatments significantly increased crowning indexes

(p=0.001) relative to either the thin-from-below to 9 inches treatment or

untreated conditions.

The distribution of treated acres in terms of crowning index also varied

among prescriptions. While all three hazard reduction approaches increased

Fiedler and Keegan Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico

High

61%

Moderate

31%

Low

8%
USDA Forest Service Proceedings RMRS-P-29. 2003. 43

Figure 2—Average composition of existing high-hazard conditions in terms of trees per acre

by diameter class and species.

Table 1—Effectiveness of hazard reduction treatments for increasing crowning index within shortinterval, fire-adapted forests in New Mexico.

Average crowning Average crowning Average crowning

Hazard reduction index before index after index 30 years after

treatment treatment (std. dev.) treatment (std. dev.) treatment (std. dev.)

Thin-from-below 16 (6.0) 39 (12.7) 37 (11.0)

Diameter-limit 16 (6.0) 61 (20.6) 57 (18.8)

Restoration 16 (6.0) 66 (20.3) 56 (16.6)

crowning indexes compared to existing conditions, the greater effectiveness

of the diameter-limit and restoration treatments is apparent (figure 3).

Crown fire hazard was reevaluated after projecting post-treatment conditions 30 years into the future. Although the effectiveness of all treatments

diminished somewhat, differences among treatments tended to persist through

time (figure 3). On average, stands that received thinning-from-below remained in the moderate-hazard range 30 years after treatment, with average

crowning index declining slightly to 37 mph (table 1). Diameter-limit and

restoration treatments retained an overall low-hazard rating, with average

crowning indexes above 50 mph (table 1).

The thin-from-below treatment was only effective in moving 18% of treated

acres to a low hazard rating (table 2). The diameter-limit and restoration treatments, in contrast, created low hazard conditions on 72% and 79% of treated

acres, respectively. The limited effectiveness of thinning-from-below is further

illustrated by the small number of treated acres (13%) that remained lowhazard 30 years following treatment. Over 60% retained that classification for

the diameter-limit and restoration treatments (table 2).

Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico Fiedler and Keegan

0

50

100

150

200

4 8 12 16 20+

Diameter Class (inches)

Trees/acre

Pine

Fir

Pinyon/Juniper

Oak

Other

Table 2—Effectiveness of hazard reduction treatments for creating low hazard conditions

in short-interval, fire-adapted forests in New Mexico.

Hazard reduction Acres rated low hazard Acres rated low hazard 30 years

treatment after treatment (percent) after treatment (percent)

Thin-from-below 18 13

Diameter-limit 72 62

Restoration 79 62
44 USDA Forest Service Proceedings RMRS-P-29. 2003.

Figure 3—Crowning indexes for pre-treatment, post-treatment year 0, and post-treatment year 30, for three

hazard reduction prescriptions applied to high-hazard stands (woodland species removed).

Fiedler and Keegan Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico

Thin-from-Below

Diameter-Limit

Restoration
USDA Forest Service Proceedings RMRS-P-29. 2003. 45

Fire Hazard: Effects of Woodland Species

Pinyon pine and juniper are a common stand component in the ponderosa

pine and dry mixed conifer forest types. Whether these species are retained or

removed in a given project depends upon treatment objectives, ownership,

and the stand context within which they occur. In contrast, gambel oak (Quercus

gambelii) is typically retained on all ownerships to serve a variety of amenity

and wildlife habitat objectives. However, retention of woodland species may

have an undesirable effect in terms of increased crown fire hazard. For this

reason, post-treatment crowning index values were evaluated for two scenarios—one with woodland species retained and the other with these species

removed (table 3). The effect of removing woodland species is substantial, with

average post-treatment crowning indexes increasing (i.e., hazard decreasing) by

16, 30, and 26 mph compared to the “retention” scenario for the thin-frombelow, diameter-limit, and restoration treatments, respectively (table 3).

Table 3—Effectiveness of hazard reduction treatments for increasing crowning index (C.I.) with woodland species

retained and removed.

Average C.I. after Average C.I. after

Hazard reduction Average C.I. before treatment, woodland treatment, woodland

treatment treatment (std. dev.) species retained (std. dev.) species removed (std. dev.)

Thin-from-Below 16 (6.0) 23 (10.3) 39 (12.7)

Diameter-Limit 16 (6.0) 31 (16.6) 61 (20.6)

Restoration 16 (6.0) 40 (24.0) 66 (20.3)

Discussion

This study represents the first statewide effort in New Mexico to estimate

fire hazard and evaluate the effectiveness of various hazard reduction treatments. It can be used both as a strategic planning tool to address broad-scale

fire hazard concerns, and as a tactical guide to help managers design effective

treatments at the project level.

It is critical that managers carefully evaluate treatment effectiveness before

selecting and applying hazard reduction treatments. For example, applying

the thin-from-below to 9 inch prescription to high hazard ponderosa pine

and dry mixed conifer forests has only a modest effect on lowering average

crown fire hazard (i.e., increasing crowning index). Furthermore, this prescription moves fewer than 20% of treated stands into a low hazard condition

after treatment, compared to 72% and 79% for the 16 inches diameter-limit

and restoration prescriptions. These results underscore the importance of evaluating pre- and post-treatment conditions (stand tables) for fire hazard during

the process of prescription development.

Crowning indexes associated with the 16 inch diameter-limit and restoration treatments differed little—either immediately after treatment or 30 years

later. However, the ecological conditions and potential sustainability associated with these two treatments will likely differ substantially over time. Under

the restoration approach, late-seral species (if present) are preferentially cut to

eliminate them as a seed source, and overall reserve density is prescribed sufficiently low to induce regeneration of ponderosa pine, thereby enhancing

sustainability. In contrast, the 16 inch diameter-limit approach neither

Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico Fiedler and Keegan
46 USDA Forest Service Proceedings RMRS-P-29. 2003.

prescribes nor allows removal of late-seral trees greater than 16 inches in diameter—trees large enough to be primary seed-producers. Furthermore, density

will generally increase over time under this treatment regime, as more and

more trees pass over the 16 inch diameter threshold and become unavailable

for cutting. Crown fire hazard will increase, and the resulting conditions will

favor establishment of late-seral species in the understory. Over decades, the

result could be a fundamental shift in forest type from ponderosa pine to more

shade-tolerant (and fire-, insect-, and disease-prone) species. Even if late-seral

species are not present, burgeoning density of overstory pines greater than

16 inch diameter will limit establishment and early development of young

pines, with fire hazard and vulnerability to beetles increasing commensurately.

A common management view of woodland species in ponderosa pine or

dry mixed conifer forests is that they are relatively innocuous in terms of their

effects on the growth and vigor of timber species, while providing a variety of

ecological, visual, and wildlife values. Results of this analysis show that regardless of hazard reduction treatment, the retention of woodland species leads to

a 16 to 30 mph decrease in average crowning index. For this reason, managers

should weigh the benefits that would accrue by retaining these species against

the substantial reduction of fire hazard that would result from removing them.

Results of this study show that the fire hazard problem in New Mexico is

best addressed by forest restoration approaches that recognize the broader

ecological context within which hazard occurs. Indeed, Fule et al. (2001)

point out that hazard reduction may be viewed as an incidental benefit of

restoration treatments given the multiple ecological benefits that accrue to

even a partially successful restoration program. Whether degraded, fire-adapted

forests are viewed from the standpoint of hazard reduction or ecological condition, an approach that centers on the density, structure, and species

composition of the reserve stand is superior to prescriptions that focus only

on the size of trees removed. The restoration prescription evaluated in this

analysis achieves greater hazard reduction and creates more sustainable conditions than alternative treatments. It is particularly superior when compared to

prescriptions with a singular focus on removal of small trees.

Acknowledgments

We thank the Joint Fire Sciences Program for funding the study “A Strategic Assessment of Fire Hazard in New Mexico” on which this paper is based.

We also thank Sharon Woudenberg, IWRIME, Ogden, UT, for valuable help

with FIA data; Steve Robertson, University of Montana, for his contribution

in data analysis; and Joe Scott, Missoula Fire Sciences Lab, for help in interpreting output from the Fire and Fuels Extension.

References

Agee, James K. 1998. The landscape ecology of western forest fire regimes. Northwest

Science 72: 24-34.

Beukema, Sarah J.; Greenough, Julee A.; Robinson, Donald C.E.; Kurz, Werner A.;

Reinhardt, Elizabeth D.; Crookston, Nicholas L.; Brown, James K.; Hardy, Colin

C.; Stage, Albert R. 1997. An introduction to the Fire and Fuels Extension to

FVS. Proceedings of a symposium; 1997 February 3-7; Fort Collins, CO. Teck,

Richard; Moeur, Melinda; Adams, Judy, eds. Gen. Tech. Rep. INT-373. Ogden,

Fiedler and Keegan Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico
USDA Forest Service Proceedings RMRS-P-29. 2003. 47

d) after applying a restoration

prescription.

Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico Fiedler and Keegan

Figure 4—Stand visualizations of:

a) existing conditions in forests rated

high for crown fire hazard,

b) after applying a thin-from-below to

9” prescription,

c) after applying a 16” diameter-limit

prescription, and
48 USDA Forest Service Proceedings RMRS-P-29. 2003.

Fiedler and Keegan Reducing Crown Fire Hazard in Fire-Adapted Forests of New Mexico

UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and

Range Experiment Station. 191-195.

Covington, Wallace W.; Moore, Margaret M. 1994. Southwestern ponderosa pine

forest structure: Changes since Euro-American settlement. Journal of Forestry 92:

39-47.

Crookston, Nicholas L. 1990. User’s guide to the event monitor: Part of prognosis

model version 6. Gen. Tech. Rep. INT-275. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Research Station. 21 p.

(DOI) Department of the Interior. 2001. “Integrating Fire and Natural Resources

Management—A Cohesive Strategy for Protecting People by Restoring Land

Health” (http://fireplan.gov/references).

Dombeck, Michael. 1997. Statement to Senate Committee on Energy and Natural

Resources, Feb. 25, 1997. U.S. Congress, Washington, DC.

Fiedler, Carl E.; Keegan, Charles E.; Arno, Stephen F.; Wichman, Daniel P. 1999.

Product and economic implications of ecosystem restoration. Forest Products

Journal 49: 19-23.

Fiedler, Carl E.; Arno, Stephen F.; Keegan, Charles E.; Blatner, Keith A. 2001.

Overcoming America’s wood deficit: An overlooked option. BioScience 51:

53-58.

Fule, Peter Z.; Waltz, Amy E.M.; Covington, Wallace W.; Heinlein, Thomas A. 2001.

Measuring forest restoration effectiveness in reducing hazardous fuels. Journal of

Forestry 99: 24-29.

Hochberg, Yosef; Tamhane, Ajit C. 1987. Multiple comparison procedures. John

Wiley and Sons, NY. 450 p.

Scott, Joe H.; Reinhardt, Elizabeth D. 2001. Assessing crown fire potential by linking

models of surface and crown fire behavior. Res. Pap. RMRS-RP-29. Fort Collins,

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research

Station. 59 p.

Stage, Albert R. 1973. Prognosis Model for stand development. Res. Pap. INT-137.

Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment

Station.

(USFS) United States Forest Service. 2000. “Protecting People and Sustaining

Resources in Fire-adapted Ecosystems—A Cohesive Strategy” (http://

www.fs.fed.us/pub/fam/).

Van Dyck, Michael G. 2001. Keyword reference guide for the Forest Vegetation

Simulator. WO-TM Service Center, USDA Forest Service. Fort Collins, CO. 97 p.

Western Governors’ Association. 2001. “A Collaborative Approach for Reducing

Wildland Fire Risks to Communities and the Environment—10-Year

Comprehensive Strategy” (http://www.westgov.org/wga/initiatives/fire/

final_fire_rpt.pdf).
USDA Forest Service Proceedings RMRS-P-29. 2003. 49

Definition of a Fire Behavior Model Evaluation

Protocol: A Case Study Application to Crown

Fire Behavior Models

Miguel G. Cruz1, Martin E. Alexander2, and Ronald H. Wakimoto3

Abstract—Model evaluation should be a component of the model development process, leading to a better understanding of model behavior and an increase in its

credibility. In this paper a model evaluation protocol is proposed that encompasses

five aspects: (1) model conceptual validity, (2) data requirements for model validation, (3) sensitivity analysis, (4) predictive validation (incorporating statistical tests),

and (5) model comparison. The proposed protocol was applied to evaluate fire behavior models that were developed to predict crown fire initiation and spread with

potential application in fire management decision support systems. The evaluation

protocol highlighted the limitations and the distinct behavior of the specific models

and the implications of such limitations when applying those models to support fire

management decision-making. The model limitations identified through these results helped the authors to characterize deficiencies in the state-of-knowledge of the

determinant processes involved in crown fire behavior, thereby identifying pertinent

research needs.

Introduction

Advances in fire behavior science have gradually resulted in the

development of fire models to support the decision-making of land

managers in a large array of fire management problems (Cohen 1990). The

use of such models allows managers to reduce the uncertainties associated

with applying fire as a management tool and facilitates proactive management.

The complexities associated with wildland fire phenomenology results in a

large number of unknowns. These limitations lead researchers to model a specific phenomenon as they rely on distinct simplifying assumptions and include

different independent fire environment properties as driving variables. This

results in a situation where distinct models attempting to describe the behavior of a particular process respond differently to a given set of conditions. This

is noticeable in evaluations of fuel treatment effectiveness in reducing fire

potential. Model outputs might be misleading and result in misguided management. Modification of the structure of the fuel complex has been the main,

if not the only, method by which fire managers can reduce the fire potential of

a given area (Countryman 1974). When the changes that a fuel treatment

causes in fire behavior are evaluated, three approaches can be identified: (1)

analysis of post fire damage in adjacent treated and untreated stands burned

under similar burning conditions (e.g., Weatherspoon and Skinner 1995, Pollet

and Omi 2002); (2) monitoring of changes in various fire behavior determinants, such as diurnal and seasonal fuel moisture dynamics, the vertical wind

profile, fuel available for combustion, overall fuel complex structure, and

experimental fires burns in the various study plots to assess differences in fire

1

Associação para o Desenvolvimento da

Aerodinâmica Industrial, Apartado

10131, 3031 – 601 Coimbra, Portugal (Present address: School of Forestry, University of Montana, Missoula,

MT).

2Canadian Forest Service, Northern

Forestry Centre, Edmonton, Alberta,

Canada.

3

School of Forestry, University of Montana, Missoula, MT.
50 USDA Forest Service Proceedings RMRS-P-29. 2003.

behavior (e.g., Alexander et al. 1991; Gould et al. 2001; Alexander and

Lanoville 2002); and (3) fire behavior simulations integrating fuel descriptors

and critical fire weather parameters (e.g., Kalabokidis and Omi 1998, Hirsch and

Pengelly 1999).

Due to the nature and complexity of the interactions defining a particular

fire environment, it is unlikely that a fire environment monitoring program,

unless it was extremely comprehensive, would capture all the variability that

would be present in a particular fuel treatment setting. Although this approach

would produce the most realistic data, the associated cost limits its applicability. Hence, most evaluations of fuel treatment effectiveness are based on

simulations of potential fire behavior that rely on fire behavior models. This

approach allows integrating the effects of the determinant input variables

through their spectrum of variability. In addition, infrequent combinations of

particular fire environment variables and certain fire behavior processes that

are not easily measured in the field can accordingly be analyzed. Nevertheless,

our understanding of the processes and interactions in the system through the

use of models needs to be accompanied with a thorough description of the

assumptions and main limitations of the models employed.

Most of the studies that have analyzed the effects of silvicultural/fuel treatment practices on fire hazard at the stand level rely solely on changes in the

structure of the fuel complex to infer potential fire behavior (e.g., Fahnestock

1968, Alexander and Yancik 1977, van Wagtendonk 1996, Kalabokidis and

Omi 1998, Scott 1998b, Stephens 1998, Hirsch and Pengelly 1999, Fulé et

al. 2001, Fiedler et al. 2001). In general, the fire simulations that support

these studies have not taken into account the possible effects of changes in

fuel moisture gradients and sub-canopy wind flow between treated and untreated situations. Some of these studies produced conflicting results due to

degree of comprehensiveness taken in modeling a particular situation and the

nature and characteristics of the fire behavior models used to carry out the

simulations.

The objective of this paper is to define a model evaluation protocol that

results in a better understanding of the use of fire behavior models in these

applications, including their limitations and biases, which we hope will eventually increase their credibility when used in evaluating effectiveness of fuel

treatments for reducing fire behavior potential.

Defining Criteria for Model Evaluation

We believe that testing and evaluation of models should be a fundamental

component of the model development process. These activities assume particular importance in fire behavior science due to the inherent difficulties in

measuring and understanding some of fire’s determinant processes. In spite of

this, model evaluation has not received much emphasis by fire behavior modelers and no comprehensive model evaluation protocol has been applied

previously to fire behavior models. Most fire behavior model evaluation that

has been done has been limited to two areas: (1) comparisons of model predictions with observed and measured fire behavior data (e.g., Brown 1972,

Lawson 1972, Bevins 1976, Sneeuwjagt and Frandsen 1977, Hough and Albini.

1978, Andrews 1980, Brown 1982, Norum 1982, Rothermel and Rinehart

1983, van Wagtendonk and Botti 1984, Albini and Stocks 1986, van Wilgen

and Wills 1988, Gould 1991, Hirsch 1989, Marsden-Smedley and Catchpole

1995, Alexander 1998, Grabner et al. 2001); and (2) sensitivity analysis studies

Cruz, Alexander, and Wakimoto Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models
USDA Forest Service Proceedings RMRS-P-29. 2003. 51

(e.g., Bevins and Martin 1978; Trevitt 1991, Scott 1998a, Cruz 1999). Critical examination of these studies reveals that they typically use subjective and/or

qualitative measures of model performance and often lack validation standards.

The process of model evaluation has been approached differently by authors due in part to philosophical interpretations as to what constitutes a model.

Models cover a large spectrum of idealizations and complexity, ranging from a

model being considered as a mathematical interpretation of a theory/hypothesis bound by certain assumptions to models that are regarded as simple

algebraic expressions that reflect a certain dataset. An important aspect to

consider in model evaluation is the definition of the criteria that should be

followed, which depends on the type of model being evaluated and its potential application. When fire behavior science is considered, theoretical models

developed to understand certain physical and chemical phenomena (e.g.,

Grishin 1997, Linn 1997) should be evaluated in a different manner than

models built to support fire management decision-making (e.g., Rothermel

1972, Albini 1976, Alexander 1988, Forestry Canada Fire Danger Group

1992). Theoretical models can be viewed as mathematical descriptions of certain physical fire phenomena and so the emphasis on evaluation should be

placed on behavioral validation instead of the numerical precision of the result

(Bossel 1991). The unknowns involved in some heat transfer and fluid dynamics processes coupled with our inability to reliably measure certain

fundamental fire quantities limit any attempt to evaluate these models by direct comparison with independently derived data. In this paper, we emphasize

the latter group of models—i.e., simple empirical or semi-empirical models

(Catchpole and de Mestre 1986) that combine physical laws with empirical

data to generate important model components describing particular physical

fire phenomena. When these models are integrated, they produce workable

models or systems that can be used to support operational fire management

decision-making.

We consider model evaluation as follows: “The substantiation of a model

within its domain of applicability possesses a satisfactory range of accuracy

consistent with the intended application of the model” (Sargent 1984). Based

on an examination of the previously referred fire behavior model evaluation

studies and other validation studies of engineering and ecological models (e.g.,

Mayer and Butler 1993, Oderwald and Hans 1993, Bacsi and Zemankovics

1995), a model evaluation protocol based on the approaches of Rykiel (1996)

and Sargent (1984) was defined for the purposes of the present work. This

protocol includes the following analyses:

Model conceptual validity—This involves analysis of the conceptual structure and logic of a model. By taking into account the intended use of the

model, we aim to determine the validity of the model’s various theories and

simplifying assumptions in capturing the dynamics of the system.

Data validation—This includes definition of data quality standards, namely

the selection of real world data that represent the phenomena of interest for

use in predictive validation, statistical validation, and model comparison. This

aspect assumes particular importance when analyzing fire behavior data due

to the relative inaccuracy and bias that arise from inherent difficulties in capturing reliable fire behavior data from either experimental fires, operational

prescribed fires, or wildfires.

Sensitivity analysis—This consists of analyses to reveal the relative influence of model components and input parameters on the behavior of the model

overall. This also includes identification of parameters that cause minor or

major fluctuations in model outputs.

Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models Cruz, Alexander, and Wakimoto
52 USDA Forest Service Proceedings RMRS-P-29. 2003.

Predictive validation—This involves comparison of model outputs with

an independent dataset of the phenomena under study in order to evaluate

model suitability to predict system behavior. This section of the model

evaluation incorporates a variety of statistical tests conducted in order to quantitatively evaluate model performance.

Model comparison—This involves a comparison of outputs from several

models describing the same phenomena thereby providing an understanding

of possible model deficiencies and the limits of applicability. In the present

study a comparison of model behavior and validity was done concurrently

with the predictive and statistical validations.

In order to better understand the evaluation protocol being proposed, we

selected a case study application of the methodology to crown fire initiation

and spread models. The models analyzed were Van Wagner (1977), Alexander

(1998) and Cruz (1999) for crown fire initiation; and Rothermel (1991),

Forestry Canada Fire Danger Group (1992) and Cruz (1999) for crown fire

spread rate. The formulation of the two Cruz (1999) models is based on

future research needs suggested by Alexander (1998). For the Canadian Forest Fire Behavior Prediction (FBP) System (Forestry Canada Fire Danger Group

1992), we chose fuel type specific fire spread models that can be applied to

fuel types in the western United States, namely FBP System Fuel Type C-3

[mature jack (Pinus banksiana) or lodgepole pine (Pinus contorta)], C-6 [Conifer plantation] and C-7 [ponderosa pine (Pinus ponderosa)/Douglas-fir

(Pseudotsuga menziesii)]. The emphasis of the present study is on establishing

a model evaluation protocol rather than a comprehensive analysis of all available models (e.g., Xanthopoulos 1990, Van Wagner 1993). Some fire behavior

prediction systems commonly used to evaluate fuel treatments, such as

FARSITE (Finney 1998) and NEXUS (Scott 1999), integrate some of the

models mentioned above with their specific interpretations of certain fire behavior processes. As a result, the models in these systems can to a certain

extent be viewed as distinct from the original model formulations being analyzed here. Nevertheless, the present study is concerned with the core models

driving the systems (i.e., crown fire rate of spread and initiation models). We

have assumed that those particular interpretations imbedded in FARSITE and

NEXUS do not induce significant changes in the outputs to warrant inclusion

in the present comparison. Consequently, the evaluation procedures throughout this study are applied solely to the aforementioned models.

Model Conceptual Validity

The analysis of the validity of model theories and assumptions is of particular importance in models intended to support fire management decisionmaking. Inappropriate use of the models could lead to detrimental and long

lasting effects on the ecosystem. Given the unknowns in fire science and difficulty in correctly measuring certain fundamental fire quantities and processes,

any analysis of the theories and simplifying assumptions embodied in a model

is limited by the current state-of-knowledge, availability of complete and reliable data sets, and our inability to propose more realistic theories. A detailed

examination of the underlying theories of the various models considered here

is outside of the scope of this paper. Consequently only a discussion of the

most restrictive assumptions of the models will be presented.

A brief introductory description of the various models follows. See table 1

for a listing of the inputs involved in the crown fire initiation models. Both the

Cruz, Alexander, and Wakimoto Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models
USDA Forest Service Proceedings RMRS-P-29. 2003. 53

Van Wagner (1977) and Alexander (1998) models are based on convective

plume theory (cf. Yih 1953) and air temperature (as a surrogate of convective

heat flux) decay with height above a linear heat source. The outputs of both

the Van Wagner (1977) and Alexander (1998) models are deterministic in

nature. The canopy ignition requirements are stated in terms of a critical fireline

intensity (as per Byram 1959), as a function of a heat sink (evaluated differently in the two models), and height above the ground. The Alexander (1998)

model, although based on the same convective plume theory, relies on a more

realistic heat source and heat sink definition and takes into account the interaction between the convective plume and the cross wind with the subsequent

tilting of the plume and dilution of the hot gases in the plume. The Cruz

(1999) crown fire initiation model is the result of an application of logistic

regression analysis to an experimental database (n = 73) and involved both

surface and crown fires used in the development of the Canadian FBP System

(Forestry Canada Fire Danger Group 1992) in order to predict the onset of

crowning. Within this model the vertical stratification of the fuel complex was

slightly modified from the commonly accepted definition of a canopy base

height (CBH) to the definition of a new fuel complex descriptor, namely fuel

strata gap (FSG). This parameter incorporates the effect of dead aerial fuels

(canopy base height is associated with live canopy fuels) in reducing the distance between surface fuels and ladder fuels that can support vertical fire

propagation. The introduction of this variable was justified by the dependence of canopy base height on just live foliage (Sando and Wick 1972, Kilgore

and Sando 1975, McAlpine and Hobbs 1994, Ottmar et al. 1998) and consequently not incorporating the effect of dead fuels in lowering the distance

between the surface and canopy fuel layers.

The Rothermel (1991) crown fire rate of spread model is the result of a

simple average correction factor relating predicted surface fire behavior by the

BEHAVE system (Andrews 1986) using Fuel Model 10 (Anderson 1982)

with wind speed adjustment factor set equal to 0.4 and a series of observations

of spread rates garnered from wildfires (n = 8). This model does not incorporate any stand structure descriptor in its formulation. The Canadian FBP System

models for predicting crown fire rate of spread are based on a sigmoid equation taking into account certain components of the Canadian Forest Fire

Weather Index System (Van Wagner 1987) that relate to the potential for fire

spread as determined by the moisture content of fine fuels and wind speed.

The Cruz (1999) crown fire spread model is based on non-linear regression

analysis parameterized using data from high intensity experimental crown fires.

This model differentiates crown fires spreading as either continuous or intermittent based on Van Wagner’s (1977) spread rate criterion for active crowning.

Table 1—Input requirements for the crown fire initiation models being evaluated1.

Fireline Wind Residence

Model intensity FMC speed time CBH/FSG EFFM SFC

Van Wagner (1977) X X X

Alexander (1998) X X X X X

Cruz (1999) X X X X

1

FMC= foliar moisture content; CBH= canopy base height; FSG = fuel strata gap; EFFM = estimated

fine dead fuel moisture content; and SFC = surface fuel consumption.

Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models Cruz, Alexander, and Wakimoto
54 USDA Forest Service Proceedings RMRS-P-29. 2003.

Data Validation

The very nature of crown fires leads to various difficulties, either institutional, social, or experimental, in acquiring reliable outdoor fire behavior

data (Alexander and Quintilio 1990) for model development, calibration,

and evaluation. Fire behavior data can be gathered from operational prescribed

fires or outdoor experimental fires (e.g., Stocks 1987, 1989) and wildfires

(e.g., Alexander and Lanoville 1987). Both types of data sources have inherent limitations. The spectrum of fire environment conditions covered by

experimental fire data normally does not include extreme fire weather conditions (e.g., high winds or severe drought). Furthermore, data garnered from

wildfires is often incomplete or lacks detail and may not be reliable. Two different types of data were used in the present study: (1) fire behavior data in

order to perform the predictive validation and then apply statistical tests and

(2) weather and fuel complex data for use in the model sensitivity analysis and

model behavior comparisons. The use of experimental fire behavior data would

be appropriate in the current evaluation exercise given the generally high reliability that characterizes such data. Both the Cruz (1999) crown fire initiation

and crown fire rate of spread models were developed using an extensive experimental fire dataset from published and unpublished sources in Canada

supplemented with a few observations from Australia. Unfortunately no experimental crown fire behavior datasets are currently available for use in

independently model testing. Consequently predictive validation and statistical tests will be based on wildfire data garnered from published cases studies

(Alexander et al. 1983, Simard et al. 1983, Lanoville and Schmidt 1985, De

Groot and Alexander 1986, Rothermel and Mutch 1986, Alexander and

Lanoville 1987, Stocks 1987, Stocks and Flannigan 1987, Hirsch 1989,

Alexander 1991). The use of such data will limit evaluation procedures applied to the Canadian FBP System fuel type specific models, as these were

originally parameterized with data from some of those same documented wildfires.

The definition of fire environment scenarios used to characterize baseline

data for sensitivity analysis and model comparison was based on well documented burning conditions where all relevant input variables were measured

or acceptably estimated. This reliable and compatible data constitutes a benchmark dataset for which model behavior can be compared. For the sensitivity

Table 2—Baseline values used in the sensitivity analysis.

Kenshoe Lake Exp.

Fire 5 Lily Lake Fire PFES Exp. Fire R1

(Stocks 1987) (Rothermel 1983) (Van Wagner 1977)

10-m open wind speed (km/h) 29 37 15

Within stand wind speed (km/h) - - 5

1-hr TL FM (%)1 8 5 10

10-hr TL FM (%)2 9 6 11

100-hr TL FM (%) 10 7 12

Live woody FM (%) 75 75 -

FMC (%) 100 100 100

Surface fuel consumption (kg/m2) - - 1 - 2

1 The1-hr time lag (TL) fuel moisture (FM) content values were estimated according to the procedures

described in (Rothermel 1983). 2 The 10 and 100-hr TL FM content values were assigned values of plus one and two percent points of

the value of the 1-hr TL FM as per Rothermel (1983).

Cruz, Alexander, and Wakimoto Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models
USDA Forest Service Proceedings RMRS-P-29. 2003. 55

analysis, two distinct fire scenarios were chosen: one conducive to marginal

crown fire activity (i.e., Kenshoe Experimental Fire 5 documented by Stocks

1989) and the other conducive to extreme crown fire behavior (i.e., 1979 Lily

Lake Fire as described by Rothermel 1983 and Alexander 1991). The use of

two fire scenarios (table 2) is justified due to multiple interacting factors within

the models being evaluated and possible non-linear effects in fire behavior.

The nature of the distinct modeling approaches that characterize the crown

fire initiation models under evaluation with their distinct input requirements

(table 1) constrains the type of predictive validation analysis and inter-model

comparison that can be applied. The evaluation of the Van Wagner (1977)

crown fire initiation model requires the monitoring of the transition period to

estimate the critical fireline intensity, which is very difficult to accomplish in

an experimental fire (Alexander 1998). The inter-model comparison for crown

fire initiation models requires the use of a fire situation where the distinct

input variables have been simultaneously measured. There is a scarcity of such

data in the published literature, one of the limiting factors being the absence

of data describing the vertical wind speed profile. The early experimental crown

fires carried out at Petawawa Forest Experiment Station (PFES), Ontario,

Canada (Van Wagner 1977) offers a complete description of the fuel complex

and fire environment characteristics suiting the various model input requirements. Published data from PFES Experimental Fire R1 (Van Wagner 1968,

1977) suits the present data requirements and will be used in the evaluation of

the various crown fire initiation models.

Sensitivity Analysis

By quantifying the effect of input variables, sub-models, and model parameters on model output, sensitivity analysis can: (1) expose model components

that cause the smallest and largest changes in the model output and (2) assess

the degree of uncertainty in the outputs that is associated with inaccurate

input estimation. This identifies which input parameters or model components should be most accurately estimated given their influence on the behavior

of the system. This is a relevant point in complex model systems, as the interaction between certain variables can induce large changes in the final result. A

complete sensitivity analysis scheme should combine the effect of all model

components combinations and interactions in a factorial design (Leemans

1991). The complexity of such a process has led to simplified sensitivity analysis schemes (e.g., Bevins and Martin 1978, Scott 1998a). Bartelink’s (1998)

relative sensitivity (RS) test was chosen for the present study. This parameter

can be viewed as an index calculated from the partial derivative of output

variables with respect to the perturbation of the input variable. This dimensionless result arises from the following criteria:

0.2

10% 10%

Vdef

V V RS + - - = [1]

where V+10% and V-10% are the resulting value of the critical parameter when

the value of the parameter under analysis is changed by 10 percent and Vdef is

the resulting value of the critical parameter under default conditions. The

value 0.2 is the relative range of the parameter to be analyzed. The 10% intervals were arbitrarily assigned. A RS score indicates the proportional response

Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models Cruz, Alexander, and Wakimoto
56 USDA Forest Service Proceedings RMRS-P-29. 2003.

of the model to the changes in the perturbed input parameter. A sensitivity

scale can be drawn from the results. RS scores less than 1 indicate insensitive

(<0.5) or slightly sensitive (0.5 – 1) model responses to inputs; and RS scores

larger than 1 indicate model sensitivity, which can be divided into moderate

(1 – 2) and high (>2).

The various models were run under the two baseline conditions outlined in

table 2 to cover the range of conditions over which crown fire behavior is

expected to occur. Although the simplified sensitivity analysis scheme used

does not take into account the interactions in a fully comprehensive manner as

would be obtained if one were using a full factorial design, the computation of

sensitivity scores for the two distinct burning conditions yielded an acceptable

range of variability for the relative sensitivity scores. The RS scores computed

for the crown fire initiation models (table 3) reflect the distinct modeling

approaches that were followed. The non-dynamic nature of the Van Wagner

(1977) crown fire initiation model results in no changes in RS scores between

the high and very high fire environment severity conditions, with the model

results indicating moderate sensitivity to changes in canopy base height (CBH)

and foliar moisture content (FMC). Both Alexander (1998) and Van Wagner

(1977) show the same sensitivity to canopy base height (CBH) variation, because of their similar formulations for the CBH effect. The sensitivity of the

Van Wagner (1977) model to FMC seems to be excessive. This is a result of

the pure theoretical formulation of the effect of FMC in increasing the heat

sink of a fuel particle. The FMC effect formulation in the Alexander (1998)

model is an application of the results of the Xanthopoulos and Wakimoto

(1993) laboratory experiments on foliar heating relationships and yields lower

sensitivity scores. A more complete analysis of the Van Wagner (1977) and

Alexander (1998) crown fire initiation models, both of which are based on

convective heat transfer theory, should include a link with a fire spread model

for the estimation of fireline intensity, as done by Scott (1999). However,

such analysis would include the errors inherent to such models for estimation

of surface fire spread (Rothermel 1972, Albini 1976) and fireline intensity

(Byram 1959, Andrews 1986), which would confound further analysis. The

RS scores from the Cruz (1999) crown fire initiation model should be analyzed considering the shape of the cumulative probability curve that is the

outcome of a logistic regression model. The higher magnitude RS scores are

relative to the steepest component of the probability curve, which is indicative

of transitional behavior, whereas the very low sensitivity values are relative to

Table 3—Relative sensitivity (RS) values associated with the crown fire

initiation model outputs for the major input parameters.

Fire environment severity

Input parameters High Very high

Van Wagner (1977)

CBH 1.5 1.5

FMC 1.3 1.3

Alexander (1998)

CBH 1.5 1.5

FMC 1 0.4

Within stand wind speed 0.6 1.4

Flame front residence time - 0.5 - 1.1

Cruz (1999)

FSG - 2.8 - 0.2

Fine dead fuel moisture - 2.4 - 0.1

10-m open wind speed 2.6 0.1

Cruz, Alexander, and Wakimoto Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models
USDA Forest Service Proceedings RMRS-P-29. 2003. 57

the flatter regions of the curve, which is characteristic of low (< 0.15) or high

(> 0.85) probability scores. This model produces the highest sensitivity scores,

making it more prone to amplifying errors due to inaccurate assessment of fire

environment input variables.

The crown fire rate of spread models analyzed can be more easily compared

due to the commonality of outputs forms and most inputs. Comparisons reveal the models show distinct sensitivities to most of the input variables. The

sigmoid equation used in the Canadian FBP System fuel type-specific models

result in the same characteristic as the one described for the logistic crown fire

initiation model of Cruz (1999). The FBP System fuel type-specific models

are extremely sensitive (RS magnitudes between 5.6 to 2.2) to changes in

input parameters on the steepest region of the sigmoid curve, which characterizes a transition from surface fire to crown fire spread (table 4). The very

high RS scores for the FMC, seen only for FBP System Fuel Type C-6 (3.6 to

1.7), seem unusually high for an unsubstantiated relationship (Van Wagner

1998). Both Alexander (1998) and Cruz (1999) found no evidence for a

significant FMC effect on crown fire spread rate. The Rothermel (1991) crown

fire spread model responds to environmental changes in the same way as the

Rothermel (1972) fire spread model. It shows very low sensitivity to changes

in fine dead fuel moisture (RS of -0.2) and moderate sensitivity (RS between

1.3 and 1.4) to wind speed. The Cruz (1999) crown fire rate of spread model,

Table 4—Relative sensitivity (RS) values associated with crown fire rate of

spread model outputs for the major input parameters.

Fire environment severity

Input parameters High Very high

Rothermel (1991)

Fine dead fuel moisture - 0.2 - 0.2

10-m open wind speed 1 1.4 1.3

Forestry Canada Fire Danger Group (1992)

FFMC2 - 5.6 / - 2.3 - 0.6 / - 0.2

10-m open wind speed 5.4 / 2.2 1.4 / 0.3

FMC - 3.6 - 1.7

Cruz (1999)

Canopy bulk density 0.2 0.2

10-m open wind speed 0.9 0.9

Fine dead fuel moisture - 1.4 - 0.9

1 10-m open wind speed was converted into 6-m (20 ft) wind speed by the

15 % adjustment factor as determined by Turner and Lawson (1978). 2 FFMC = Fine fuel moisture code.

based on a large database of experimental crown fires, is an image of its dataset.

This model’s RS scores vary from very low for canopy bulk density (0.2) to

moderate for fine dead fuel moisture (-1.4). Wind speed has a proportional

effect on rate of spread in this model.

Predictive Validation

Predictive validation is applied here to determine: (1) the model adequacy

in capturing the behavior of the real world system under study; and (2) if the

Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models Cruz, Alexander, and Wakimoto
58 USDA Forest Service Proceedings RMRS-P-29. 2003.

model accuracy is suitable for its proposed application. Such tests should be

done using independent, highly reliable data to decrease the probability of a

Type I error (Sargent 1984), rejecting the validity of a valid model. All the

high-intensity experimental fire behavior (either near or above the threshold

of crowning) in the literature readily available to the authors was used to develop two of the models (Cruz 1999) under analysis. In order to simultaneously

compare the various models under a common basis, independent, high-intensity wildfire data was utilized. The characteristics of wildfire derived data

generally make it unsuitable for the evaluation of crown fire models (e.g.,

characteristics lack precise description of fuel complexes and fires spreading

under the influence of extreme weather conditions). Consequently, a slightly

different approach was followed in the evaluation of crown fire initiation models.

The predictive validation of such models was based on the analysis of the

model behavior under a well-documented fire situation where all input variables needed to characterize a fire scenario for the various models were either

measured and/or acceptably estimated. The crown fire model input requirements from PFES Experimental Fire R1 (Van Wagner 1968, 1977) are

presented in table 2. The comparison between models was based on the 10-m

open wind speed requirements given variable vertical stratification in the fuel

complex, with CBH values of 2, 4 and 6 m. For these fire environment scenarios, the critical fireline intensities for Van Wagner (1977) and Alexander

(1998) models, respectively, are as follows: for CBH = 2 m, 475 and 540 kW/

m; for CBH = 4 m, 1344 and 1290 kW/m; and for CBH = 6 m, 2470 and

2180 kW/m. The critical fireline intensities for Alexander (1998) model were

based on a residence time of 45 seconds as observed in R1, and a constant of

proportionality of 16 as determined for needlebed surface fuel complexes

(Alexander 1998). The estimation of fireline intensity for use as input in these

two models was based on the output of the two principal fire behavior prediction systems used in North America, the BEHAVE System (Andrews 1986)

and the Canadian FBP System (Forestry Canada Fire Danger Group). The

use of these two systems to predict a fundamental fire behavior descriptor

highlights the potential error propagation problem when using the Byram’s

(1959) fireline intensity as an input variable for determining the requirements

for crown fire initiation.

As pointed out previously, any comparison between the crown fire initiation models is hindered by differences in model formulations and the

dependence of some of the models on fire behavior quantities that must be

estimated in advance as inputs in the prediction. The error propagation

Table 5—The 10-m open wind speeds (km/h) required for crown fire initiation based on

variation in CBH (m). Fixed burning conditions associated with PFES Experimental Fire

R1 as described by Van Wagner (1968).

Crown fire initiation models

Van Wagner (1977) Alexander (1998) Cruz (1999)

CBH (m) BEHAVE FBP C-6 1 BEHAVE FBP C-6 1

2 24 (8)2 1 27 (9)2 0 7

4 48 (16) 4 45 (15) 4 10.5

6 75 (25) 10 69 (23) 9 14.1

1

 FFMC = 92 and BUI = 70 (Alexander 1998).

2 Mid-flame wind speeds as required for input in the surface fire spread model embodied in

the BEHAVE System (Andrews 1986). Conversion from within stand (1.2-m) to 10-m

open wind speed based on linear transformation using a 3.0:1 ratio as measured during

R1 experimental fire (Van Wagner 1968, Alexander 1998).

Cruz, Alexander, and Wakimoto Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models
USDA Forest Service Proceedings RMRS-P-29. 2003. 59

problem becomes especially evident when comparing the 10-m open wind

speed required to attain the fireline intensity requirements for crown ignition

for the two surface fire spread models tested (table 5). Part of that error is

produced by the manner in which fireline intensity is estimated and the distinct models used to estimate surface fire rate of spread. The wind speed

requirements for crown ignition using the BEHAVE system seem to be unreasonably high at 48 km/h for a CBH of 4 m and 75 km/h for a CBH of 6

m (table 5), a problem that is also evident in Scott’s (1998b) analysis and

Scott and Reinhardt’s (2001) simulations. In contrast, the use of the FBP

System Fuel Type C-6 fire spread model yielded what seems to be very low

wind speed requirements for crown ignition. The 1 and 4 km/h open wind

conditions would result in almost no wind flow within the sub-canopy space,

resulting in low intensity surface fires that would hardly induce crown combustion for the fuel complex simulated. These differences in response arise

from the fact that the BEHAVE System estimates fireline intensity from the

product of reaction intensity (quantity evaluated under no wind, no slope in a

laboratory setting) with flame depth (Rothermel 1972, Albini 1976, Andrews

1986). This estimation procedure yields systematically lower values than the

original Byram (1959) formulation that was used by Van Wagner (1977) to

determine the empirical proportionality constant in his crown fire initiation

model. Consequently this inflates the wind speed requirements needed to attain the critical fireline intensities. The Cruz (1999) crown fire initiation model

appeared to yield reasonable results for the situations tested, with wind speed

requirements varying between 7 and 14 km/h for the CBH range tested. For

reference purposes, crowning associated with PFES Experimental Fire (CBH

= 7 m) was attained with a wind speed of 15 km/h (Alexander 1998).

The two crown fire initiation models based on convective plume theory

(Yih 1953) examined here (i.e., Van Wagner 1977, Alexander 1998) could be

regarded as more sound conceptually than the Cruz (1999) model and presumably lead to a greater understanding of crown fire initiation phenomenology.

However, some of the limiting assumptions concerning plume theory when

applied to free-burning wildland fires and the dependence of Byram’s (1959)

fireline intensity to define the heat source potentially limits their use as a robust model. Further focus on convective plume theory in the development of

new models of crown fire initiation may in fact stifle innovation. The relationship obtained by Yih (1953) through similarity analysis linking the temperature

at a certain height above a linear heat source is technically

restricted to still-air conditions, although Alexander’s (1998) model has

attempted to account for the cross-wind case. Neither Van Wagner (1977)

nor Alexander (1998) attempted to account for the role of radiant heat flux in

the onset of crowning from the flames that typically characterize high-intensity surface fires, although the authors readily acknowledged this shortcoming/

possible limitation in their models.

Predictive validation applied to the crown fire spread models was based on

a wildfire dataset derived from case studies. No Canadian FBP System model

was tested here due to the non-independence of these models and the wildfire

dataset used here. Although the published wildfire case studies provide general information on the crown fire runs, fuel types and weather conditions,

detailed fuel complex descriptions and quantitative data generally do not exist. For evaluation of the Cruz (1999) crown fire rate of spread model, a

nominal canopy bulk density value of 0.15 kg/m3 was assigned on the basis of

knowledge and experience with experimental fires in generally similar fuel types.

All the fires were also assumed to be spreading as active crown fires. This

assumption is corroborated by the high spread rates observed on the wildfires

Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models Cruz, Alexander, and Wakimoto
60 USDA Forest Service Proceedings RMRS-P-29. 2003.

selected for evaluation purposes. Figure 1 displays a scatterplot of observed

versus predicted rate of spread produced by the Cruz (1999) and Rothermel

(1991) crown fire rate of spread models. The results suggest strong underprediction trends for the Rothermel (1991) model and an acceptable

agreement, albeit an over-prediction trend for the Cruz (1999) model. The

over-prediction trend of the Cruz (1999) model might arise from the worst-case

scenario assumed for the crown fire run simulations that extended for several

hours. These scenarios use the lower fine fuel moisture content computed for

the fire run, whereas a more detailed fire simulation encompassing fine fuel

moisture and wind speed variability over the burning period would probably

reduce this tendency to over-predict. Hence we are not certain that the overprediction trend evident in figure 1 is the result of model bias or the inadequacy

of the test data/approach used to replicate real-world conditions. A cursory

examination of the scatterplot also reveals an inability for the Rothermel (1991)

model to predict high rates of spread for many of the situations considered

(i.e., for many of the wildfires the model seldom predicted a spread rate greater

than 10 m/min while the observed spread rates varies from 10 to nearly 50

m/min). In order to quantify the adequacy of the model’s behavior, two deviance measures were sought: the mean absolute error (MAE) and the mean

absolute percent error (MA%E) (Mayer and Butler 1993, Cruz 1999). For

the dataset utilized in the present analysis, the mean absolute errors computed

were 20 m/min (and MA%E of 62%) for the Rothermel (1991) model and

9.2 m/min (and MA%E of 34%) for the Cruz (1999) crown fire spread model.

Comparing Two Models

The statistical validation procedures complement some of the quantitative

results obtained from the descriptive analyses previously performed on the models. Nevertheless, the definition of any statistical validation criteria is hampered

Cruz, Alexander, and Wakimoto Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90

Observed rate of spread (m/min)

Predicted rate of spread (m/min)

Cruz (1999)

Rothermel (1991)

Figure 1—Plot of observed versus

predicted rate of spread of crown

fires in the Rothermel (1991) and

Cruz (1999) crown fire spread

models.
USDA Forest Service Proceedings RMRS-P-29. 2003. 61

by the difficulty in defining adequate tests and appropriate confidence levels

for the phenomena under study. Different tests might produce conflicting

results, accepting or rejecting the same hypothesis simultaneously. The 0.05

alpha levels commonly accepted for statistical significance in a variety of natural resources studies might not be adequate to analyze phenomena that may

vary several orders of magnitude. The previously described limitations in the

datasets used here that prevent an independent analysis of the crown fire initiation models also restrict the application of statistical tests to the crown fire

rate of spread models. Using the wildfire dataset employed in the Predictive

Validation section, the models were analyzed for: (1) their modeling efficiency;

(2) linear regression parameters; and (3) simultaneous F-test for slope = 1 and

intercept = 0 (Draper and Smith 1981, Mayer et al. 1994). Modeling efficiency, EF, is expressed as follows (from Mayer and Butler 1993):

( )

Â ( )

Â -

- = - 2

2 ˆ 1

i i

i i

y y

y y EF [2]

where yi is the observed and i yˆ is the predicted value. This measure provides an indication of goodness of fit, with an upper bound of 1 describing a

perfect fit and values less than 0 indicating poor model performance; see Mayer

and Butler (1993) for further interpretation of EF. The simultaneous F-test

for slope = 1 and intercept = 0 evaluates the null hypothesis, H0

:(b0,b1)=(0,1),

by the following statistic: Q=( b - b)1

’X’X ( b - b), where b is the population

parameters to be tested, X’X the matrix term in the independent variable, and

b is the vector of regression parameters. The null hypothesis is accepted if Q £

ps2F (p,v,1-a), where p is the regression degrees of freedom, s2 is the variance,

and v is n – p.

The application of the two crown fire rate of spread models under comparison to the crown fire dataset (see references to the wildfire case studies in Data

Validation) produced EF values of –0.14 and 0.68 (table 6) for the Rothermel

(1991) and Cruz (1999) models respectively. The slope coefficients (b1

) resulting from the regression analysis (table 6) reflect the slight over-prediction

trend of the Cruz (1999) model and the strong under-prediction trend of the

Rothermel (1991) model. For both models the simultaneous F-test for slope

and intercept resulted in the rejection of the null hypothesis (both Q > ps2

F

(p,v,1-a)). Given the uncertainty in the input conditions for the wildfire runs

and the type of phenomena being analyzed, with rates of spread varying over

three orders of magnitude, the results from the F-test should be analyzed with

caution, as this test might be too restrictive for the phenomena under study

(i.e., the null hypothesis with slope = 1 and intercept = 0, is easily rejected).

Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models Cruz, Alexander, and Wakimoto

Table 6—Statistical validation parameters for the Rothermel (1991) and Cruz (1999) crown

fire rate of spread models.

Linear regression

ß0 ß1 Q ps2

F (p,v,1- a)

Model EF R2 (lower/upper 95%) (lower/upper 95%)

Rothermel -0.14 0.63 12.0 (2.52 / 21.5) 1.75 (1.06 / 2.44) 9231 441

 (1991)

Cruz (1999) 0.68 0.75 4.5 (-4.45 / 13.46) 0.74 (0.52 / 0.95) 1087 283
62 USDA Forest Service Proceedings RMRS-P-29. 2003.

Concluding Remarks

Models are simplified theories developed to approximate the behavior of

real world systems. The process of model evaluation acquires particular

importance when the models are used to support decision-making. This shifts

the emphasis on validation procedures to the potential applications of these

models instead of the model itself (Mayer and Butler 1993). There exists a

vast list of validation techniques available to evaluate models but hardly a set

appropriate for widespread application covering different modeling approaches.

The present set of evaluation procedures was developed to provide information about model performance and how well it replicates the behavior of the

real-world system. The theoretical basis of the models evaluated and their

distinct modeling approaches make them difficult to compare. The problem

of model conceptual validity is one of the most challenging issues. The unknowns in certain fundamental fire behavior processes make it difficult to define

which theories are most appropriate. The results of the sensitivity analysis

showed how the models respond differently to the various input variables

dominating some of the processes involved in crown fire initiation and spread.

The main differences were found in the way foliar moisture content affects the

susceptibility of canopy ignition. The theoretical model from Van Wagner

(1977) is the most sensitive to FMC, followed by the Alexander (1998) application of Xanthopoulos and Wakimoto’s (1993) laboratory experiments, which

assumes that the duration of heating can be equated to flame front residence

time. The Cruz (1999) crown fire initiation model does not incorporate the

effect of FMC, reflecting the non-significance of this parameter in the initiation of crown fires within the dataset used in its development. Fuel complex

vertical stratification appears to manifest a comparable effect on the various models for crown fire initiation. The various crown fire rate of spread models respond

similarly to changes in wind speed, but quite differently to fine fuel moisture

content. The Rothermel (1991) model is relatively insensitive to the variation in

this parameter whereas the Canadian FBP System models are over-sensitive. This

evaluation of model sub-components highlights some areas needing further research and acquisition of new laboratory and field data. Combustion

characteristics of live fuels are poorly understood and the effect of foliar moisture content as a heat sink has never been evaluated under heat flux conditions

characteristic of wildfire situations. The effect of CBH on heat flux decay in

the Van Wagner (1977) and Alexander (1998) crown fire initiation models is

solely based on convective heating and no allowance is presently made for a

radiative contribution. One would expect that a surface fire burning just below the threshold for crowning will possess an ample flame front depth, leading

to a strong effect of the radiative component in heating canopy fuels. Further

refinement of existing crown fire initiation models is required in order to

accommodate the contribution that radiation plays in the onset of crowning.

The present study has also highlighted the lack of published fire behavior

data that can be used in model evaluation. Although the growing complexity

of fire management decision-making relies on the extensive use of fire behavior models, there were virtually no studies carried out in the United States

that produced suitable data that could be used in the evaluation and calibration of models or systems used for predicting crown fire behavior.

Cruz, Alexander, and Wakimoto Definition of a Fire Behavior Model Evaluation Protocol: A Case Study Application to Crown Fire Behavior Models
USDA Forest Service Proceedings RMRS-P-29. 2003. 63

Acknowledgments

This paper benefited from the comments and suggestions of Dr. Bret Butler

and Rudy King of the USDA Forest Service and two anonymous reviewers.

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68 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 69

In Situ Soil Temperature and Heat Flux

Measurements During Controlled Surface

Burns at a Southern Colorado Forest Site

W.J. Massman1, J.M. Frank1, W.D. Shepperd1, and M.J. Platten2

Abstract—This study presents in situ soil temperature measurements at 5-6 depths

and heat flux measurements at 2-5 depths obtained during the fall/winter of 2001/

2002 at seven controlled (surface) fires within a ponderosa pine forest site at the

Manitou Experimental Forest in central Colorado. Six of these burns included three

different (low, medium, and high) fuel loadings under both a closed-canopy forested

site and an open forest with a grassy meadow understory. The fuel loading for the

seventh burn was a conical pile of slash about 6 m in height and 9 m in diameter and

was intended to duplicate the structure and loading of a slash pile resulting from

mechanical harvesting activities. One basic purpose of this initial experiment was to

assess how well some commercially available soil heat flux plates would perform at

high temperatures. The data presented here include soil temperatures, heat fluxes,

and depth and duration of the thermal energy penetration into the soils. The maximum surface heat fluxes were estimated to be about 2400 Watts/meter2 [Wm-2] at the

slash pile burn site, 2300 Wm-2 at the high fuel meadow site, and 3000 Wm-2 at the

high fuel forested site. Extrapolated surface temperatures are about 436 C at the

slash burn site, 359 C at the high fuel meadow site, and 95 C at the high fuel forested

site. Recovery of a normal daily temperature cycle depended on fire duration and

fuel loading. The recovery times were between 16 and 20 hours at the high fuel sites,

about half this time at the medium fuel sites, and less that 2 hours at the low fuel

sites. However, the recovery time at the slash pile site was about 2 weeks. Although

further tests and refinements are planned, the present results suggest not only that

soil heat flux can be reliably measured during controlled burns, but that soil temperatures and heat flux can differ significantly with different fuel loadings.

Introduction

Both natural and prescribed fires play important roles in managing and

maintaining most ecosystems in the western United States. In many

ecosystems, fire is the major cause of disturbance and change. One of the less

visually obvious changes caused by fire, even a relatively small fire, is the heat

pulse and associated high temperatures that penetrate the soil. High soil temperatures influence forests and their ability to regenerate after a fire by altering

soil properties; killing soil microbes, plant roots, and seeds; destroying soil

organic matter; and altering soil nutrient and water status and soil nutrient

cycling (Frandsen and Ryan 1986; Hungerford et al. 1991; Campbell et al.

1995; DeBano et al 1998). Some consequences of fire can be subtle and long

term (Sackett and Haase 1992; DeBano et al 1998), while others, such as

increasing soil erosion and the concomitant effects on water quality and the

hydrological cycle, are more immediate and obvious. Because fire is frequently

used by land managers to reduce surface fuels, it is important to know how

1USDA Forest Service, Rocky Mountain

Research Station Fort Collins, CO.

2USDA Forest Service, Manitou

Experimental Forest Woodland Park,

CO.
70 USDA Forest Service Proceedings RMRS-P-29. 2003.

soil properties and fuel conditions interact to determine the nature and extent

of the soil heat pulse. Ideally, quantifying this coupling between surface fuels

and the soil heat pulse requires both observational and modeling studies (e.g.,

Steward et al 1990), for which both soil temperature and soil heat flux data

are necessary for understanding the physical processes governing heat propagation through soils and for verifying model performance. Ultimately, such a

combined approach should help managers to maximize the ecosystem benefits of a prescribed fire while minimizing the potential for harm.

This report presents and discusses the initial results of a larger experiment

to evaluate how different amounts and geometrical arrangements of fuel loadings influence forest regeneration after fires. Here we specifically detail in situ

measurements of soil temperature and heat flux taken during seven controlled

burns in Manitou Experimental Forest in central Colorado. The primary

motivation for these initial burn experiments was to assess the ability of two

commercially available soil flux heat plates to operate at the high temperatures

encountered during surface fires. This is achieved in this report by examining

the data for internal consistency and by developing a simple method of extrapolating the measured soil temperature and heat flux data to surface values

during the fires.

Experimental Design

Site Location and Description

Manitou Experimental Forest is located in Teller County, central Colorado.

The site latitude and longitude are 39 ∞ 04' North and 105 ∞ 04' West with an

elevation of about 2400 m. Vegetation at the burn areas is predominantly

ponderosa pine with an understory of bunchgrasses with numerous grassy

openings throughout the area. Soils at the burn areas were either BoyettFrenchcreek-Pendant associated with 15 to 40 percent slopes with a typical bulk

density of 1.20-1.35 gm cm-3 and an air permeability of about 12 ± 6 (10-12) m2 or

Pendant cobbly loam also occuring on 15 to 40 percent slopes with a bulk density of 1.30-1.70 gm cm-3 and air permeability of about 4 ± 2 (10-11) m2 (USDA

and other agencies 1986). Except for the few soils that are derived from red

arkosic sandstone, all soils in the area are derived from biotite granite and

associated igneous rocks of the Pikes Peak batholith. Annual precipitation on

the forest is about 400 mm and the annual mean ambient temperature is

about 5 C.

Fuel Loadings and Sensor Deployment

Three sites were instrumented and then amended by adding slash of various densities. The first two sites, consisting of three plots each, were

instrumented on March 22, 2001, on the Boyett-Frenchcreek-Pendant type

soil. Three plots with low, medium, and high fuel loadings were under a closedcanopy forest (forest site) and the other three plots were within a grassy meadow

associated with a more open forest (meadow site), created as a result of a

timber harvest two years prior. The forest site was about 130 m west of the

meadow site and the plots within each site were adjacent to one another and

averaged about 70 m2

. These 6 sites were amended on April 5, 2001, and the

controlled burn occured November 9, 2001. The third or slash burn site consisted of one plot and was instrumented on October 12, 2001, amended on

October 18, 2001, and burned on January 11, 2002. It was about 200 m east

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site
USDA Forest Service Proceedings RMRS-P-29. 2003. 71

of the meadow site. The slash pile was conical in shape and approximately 9 m

in diameter and 6 m in height. The soil type at this burn site was Pendant

cobbly loam and it was located within a very large grassy meadow that had

been forested two years prior. The forest and meadow burn sites were on

south facing slopes of small 2-3 m deep draws. The fires were initiated (as

much as possible) at the bottom of the slope. The instruments were inserted

horizontally and buried in the soils at relatively more horizontal areas near the

top of the slope. The slash pile site was on a gentle west facing slope. Table 1 gives

the details on the fuel loadings at each site for pre- and post-burn conditions.

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten

All data were logged on Campbell Scientific CR23X’s (Campbell Scientific

Inc., Logan, UT). All wires connecting the sensors to the data logger were

laid in a trench which was then backfilled (as much as possible) with the original soil. The data loggers were located 10 to 30 m from the sensors. All data

loggers were enclosed in a metal fire resistance box. However, at the slash

burn site the data logger box was also buried to ensure additional protection

from the fire. Data sampling rates before and well after the fires was 1 Hz with

average data recorded every half hour. Between 2 and 4 hours before, during,

and for about 4 days after the fire, data were sampled at 4 Hz and recorded

every 15 s. Table 2 gives the type, number, and depth of the temperature and

heat flux sensors. To minimize interference between the sensors, the soil heat

flux plates and soil temperature probes were separated laterally by a few centimeters.

On the morning of the fires the moisture content of the litter layers, duff

layers, and the uppermost few centimeters of soil were measured at the six

forest and grassy meadow sites. The litter layers were 15 percent by weight

(meadow) and 23 percent by weight (forest). The duff layer was 33 percent by

weight at the meadow sites and 14 percent by weight at the forest sites. Finally, the soil moisture content was about 3 percent by weight at the meadow

site and about 2 percent by weight at the forest site. No fuel or soil moisture

data were obtained at the slash burn site.

Potential Concerns

Before presenting the observed data it is important to bear in mind the

context and the intent of this experiment. All burn sites are representative of

slash left after harvesting in the area. Therefore, we would expect the present

data are quite typical of many prescribed surface fires within the Manitou

Table 1—Fuel loadings for controlled burns at Manitou Experimental Forest. The first

number in each entry is the preburn data and the second is postburn data. NA =

Not Available.

Fuel depth Duff depth Litter depth Total loading

Site (loading) (cm) (cm) (cm) (tonsM/hectare)

Meadow (low) 5.0/0.0 1.4/0.0 5.0/0.0 8.80/0.0

Meadow (med) 19.5/2.5 0.63/0.25 5.0/2.5 23.1/7.60

Meadow (high) 30.5/0.85 0.38/0.00 9.3/0.85 68.6/7.94

Forest (low) 5.0/3.4 0.51/0.08 5.0/2.5 1.44/0.78

Forest (med) 22.0/3.4 0.25/0.08 3.4/2.5 15.0/7.11

Forest (high) 21.2/2.5 0.25/0.00 3.4/2.5 31.8/14.4

Slash pile 600a/0 NAb/NA NAb/NA 560a/0

a Estimate.

b Expected to be similar to average of meadow sites above.
72 USDA Forest Service Proceedings RMRS-P-29. 2003.

Experimental Forest and surrounding areas. However, fuel loadings for

wildfires could be quite different and could vary considerably depending primarily on recent fire history and amount of slash from previous logging

activities.

Other considerations and concerns involve the measurement of soil heat

flux and possible alterations or disruptions of the soil. For example, reliable

estimates of soil heat flux require good contact between the soil and the heat

flux plate. Any moisture present in the soil will also help maintain this contact.

However, when soil temperatures reach about 100 C, soil moisture will be

vaporized and the contact between the plate and the soil may degrade, resulting in poor data quality. Furthermore, performance of some of the soil heat

flux plates at high temperatures is questionable because they were not specifically designed for these conditions. Finally, there is the possibility that the soil

may have been disturbed when amending the plots. For these reasons, therefore, we removed all the soil temperature and heat flux plates several months

after the fires and checked the vertical placement of all the sensors and the

heat flux plate calibrations.

Results

Observed Soil Temperatures and Heat Fluxes

Soil temperatures and heat fluxes are presented as a pair of graphs for each

burn site. The figure denoted with an ‘a’ is temperature and ‘b’ refers to soil

heat flux. The first 3 figures are the meadow sites (high, medium, and low

amended). The next 3 are for the forest sites (high, medium, and low amended).

Figure 7 shows data from the slash burn site. As denoted on figure 7b two

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site

Table 2—Placement and type of soil temperature and heat flux

sensors for controlled burns at Manitou Experimental Forest. Here

T means a temperature sensor and GR and GT are soil heat flux

plates. Note: all thermocouple junctions were coated with epoxy

(Omegabond 101) for electrical isolation.

3 closed canopy sites and

3 grassy meadow sites Large slash pile

Depth (m) Sensor type Sensor type

0.02 Ta GR

d Ta

0.05 Tb Ta GT

f

0.10 Tc GR

d Tb GTf

0.15 Tc

0.30 Tc GR

d,e Tc GTf

0.50 Tc GR

d

1.365 Tc GR

d

a Omega Engineering, Stamford, CT: model no. HH-K-24, rated

704 C. b Omega Engineering, Stamford, CT: model no. TT-J-24, rated

260 C. c Omega Engineering, Stamford, CT: model no. TT-T-24, rated

200 C. d Radiation Energy Balance Systems, Seattle, WA: model no. HFT

3.1. e Not included at the low fuel loading sites.

f Thermonetics Corporation, La Jolla, CA: glazed ceramic design,

rated to 775 C, nominal sensor sensitivity 1250 to 1750 Wm-2mV-1,

cromel extension wire to data logger: Omega Engineering TFCH020, temperature rated to 260 C. Note: post-burn examination of

extension wire did not reveal any high temperature damage.
USDA Forest Service Proceedings RMRS-P-29. 2003. 73

different soil heat flux plates were used for this site. None of the data shown in

these figures have had any dropouts removed. Here data dropouts are defined

as physically unrealistic negative values, such as those shown in Figs. 1a and

3a. These dropouts are associated with checking or resetting the data logger.

Figs. 1a and 4a also show some spikes, which are thought to be related to

spurious data logger errors. Each of these figures includes one or two days

before the burn and two or more days after the burn to emphasize the dramatic change in the daily soil thermal regime caused by the fire and to give

some indication of the time required at each site to recover a new daily temperature cycle.

In general, the duration and intensity of the fires were determined primarily by the amount and type of the fuel loading and secondarily by fuel moisture

content and rate of spread. These secondary factors are discussed in the next

section. Here we examine the primary factors, the amount and type of the fuel

loading.

The data show that higher temperatures and greater magnitudes for the soil

heat flux are associated with higher fuel loading. For example, the loading at

the medium and high meadow sites is about double the medium and high

forest sites. Correspondingly, the maximum 2 cm soil temperatures are much

higher at the meadow sites for the medium (222 C) and high loadings (260 C)

than at the medium (36 C) and high (69 C) forest sites. A preponderance of

dry grass in the meadow site versus pine needles in the shaded forest site

probably accounts for the more intense, hotter fires at the meadow site than

occured at the forest sites. Even more dramatic are the differences in fuel

loadings and temperatures between the slash pile site, where the temperature

at 2 cm reached about 407 C, and the forest or meadow site. Similarly, soil

heat flux is greater (negative indicating heat flow into the soil) with increasing

fuel loading, such that the magnitudes at 2 cm reached to about 1500 Wm-2 at

the high density forest and meadow sites and more than 1700 Wm-2 at 5 cm at

the slash burn site.

Likewise, the depth of thermal energy penetration and the duration of the

heat pulse increased with fire duration and intensity and, ultimately, fuel loading. The duration of the heat pulse can be estimated from the time required

for the transient heat pulse associated with the fire to dissipate, re-establishing

a daily thermal cycle after the burn. Although these recovery times do vary

with depth, in general the data suggest that they were between 16 and

20 hours at the high fuel sites, about half this time at the medium fuel sites,

and less that 2 hours at the low fuel forest site. In contrast, the recovery time

at the slash pile site was about 2 weeks. The deepest measured penetration of

thermal energy occurs at the slash pile site where the temperature and heat

flux at 1.365 m began increasing several days after the fire.

The only exception to this general association between the soil thermal

response and fuel loading appears to be the low fuel meadow site. At this site

it appears that the fire dynamics and the micro structure of the fuel loading

prevented the fire from having any measurable influence on the soil. This

occured in spite of the fact that the fuel loading was about 6 times the fuel

loading of the low fuel forest site. The fire was intense at this site but extremely brief. It flashed over the burial location of the sensors in just a few

seconds and had mostly burned itself out in less than a minute or two. Therefore, it appears that the fire duration was too short to have had much effect on

the soil (at least at 2 cm and below).

Another possible anomaly occurs with the 30 cm soil heat flux during the

slash pile fire. The data are noticeably noisier and for a time are directed upward (away from the soil), rather than into the deeper soil levels. The noisy

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten
74 USDA Forest Service Proceedings RMRS-P-29. 2003.

 -10

 -5

 0

 5

 10

 15

 20

311 312 313 314 315 316

Soil Temperature (oC)

Day of Year 2001

2 cm

5 cm

10 cm

15 cm

30 cm

Figure 1—Five days of recorded

soil temperatures (a) and heat

fluxes (b) at the low fuel meadow

site. Time series include two days

before the fire and include two

days after the fire. The fire was

initiated about 1:20 PM MST on

day 313 of 2001 and was over by

about 1:25 PM. The soil heat flux

was measured using Radiation

Energy Balance Systems heat flux

plates.

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site

 -60

 -40

 -20

 0

 20

 40

311 312 313 314 315 316

Soil Heat Flux (W m-2)

Day of Year 2001

2 cm

10 cm
USDA Forest Service Proceedings RMRS-P-29. 2003. 75

 -25

 0

 50

 100

 150

 200

 225

311 312 313 314 315 316

Soil Temperature (oC)

Day of Year 2001

2 cm

5 cm

10 cm

15 cm

30 cm

Figure 2—Five days of recorded

soil temperatures (a) and heat

fluxes (b) at the medium fuel

meadow site. Time series include

two days before the fire and

include two days after the fire. The

fire was initiated about 1:30 PM

MST on day 313 of 2001 and was

over by about 1:40 PM. The soil

heat flux was measured using

Radiation Energy Balance Systems

heat flux plates.

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten

-350

-300

-250

-200

-150

-100

 -50

 0

 50

311 312 313 314 315 316

Soil Heat Flux (W m-2)

Day of Year 2001

2 cm

10 cm

30 cm
76 USDA Forest Service Proceedings RMRS-P-29. 2003.

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site

-1600

-1400

-1200

-1000

-800

-600

-400

-200

 0

 100

311 312 313 314 315 316

Soil Heat Flux (W m-2)

Day of Year 2001

2 cm

10 cm

30 cm

 -25

 0

 50

 100

 150

 200

 250

 275

311 312 313 314 315 316

Soil Temperature (oC)

Day of Year 2001

2 cm

5 cm

10 cm

15 cm

30 cm

Figure 3—Five days of recorded

soil temperatures (a) and heat

fluxes (b) at the high fuel meadow

site. Time series include two days

before the fire and include two

days after the fire. The fire was

initiated about 1:48 PM MST on

day 313 of 2001 and was mostly

over by about 2:20 PM. The soil

heat flux was measured using

Radiation Energy Balance Systems

heat flux plates.
USDA Forest Service Proceedings RMRS-P-29. 2003. 77

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten

 -10

 0

 10

 20

 30

 40

311 312 313 314 315 316

Soil Temperature (oC)

Day of Year 2001

2 cm

5 cm

10 cm

15 cm

30 cm

Figure 4—Five days of recorded

soil temperatures (a) and heat

fluxes (b) at the low fuel forest site.

Time series include two days

before the fire and include two

days after the fire. The fire was

initiated about 11:15 AM MST on

day 313 of 2001 and was over by

about 11:40 AM. The soil heat flux

was measured using Radiation

Energy Balance Systems heat flux

plates.

-350

-300

-250

-200

-150

-100

 -50

 0

 50

311 312 313 314 315 316

Soil Heat Flux (W m-2)

Day of Year 2001

2 cm

10 cm
78 USDA Forest Service Proceedings RMRS-P-29. 2003.

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site

 0

 10

 20

 30

 40

311 312 313 314 315 316

Soil Temperature (oC)

Day of Year 2001

2 cm

5 cm

10 cm

15 cm

30 cm

Figure 5—Five days of recorded

soil temperatures (a) and heat

fluxes (b) at the medium fuel forest

site. Time series include two days

before the fire and include two

days after the fire. The fire was

initiated about 11:41 AM MST on

day 313 of 2001 and was over by

about 12:10 PM. The soil heat flux

was measured using Radiation

Energy Balance Systems heat flux

plates.

-1200

-1000

-800

-600

-400

-200

 0

 100

311 312 313 314 315 316

Soil Heat Flux (W m-2)

Day of Year 2001

2 cm

10 cm

30 cm
USDA Forest Service Proceedings RMRS-P-29. 2003. 79

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten

 0

 10

 20

 30

 40

311 312 313 314 315 316

Soil Temperature (oC)

Day of Year 2001

2 cm

5 cm

10 cm

15 cm

30 cm

Figure 6—Five days of recorded

soil temperatures (a) and heat

fluxes (b) at the high fuel forest site.

Time series include two days

before the fire and include two

days after the fire. The fire was

initiated about 12:11 PM MST on

day 313 of 2001 and was over by

about 12:40 PM. The soil heat flux

was measured using Radiation

Energy Balance Systems heat flux

plates.

-1600

-1400

-1200

-1000

-800

-600

-400

-200

 0

 100

311 312 313 314 315 316

Soil Heat Flux (W m-2)

Day of Year 2001

2 cm

10 cm

30 cm
80 USDA Forest Service Proceedings RMRS-P-29. 2003.

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site

 -25

 0

 50

 100

 150

 200

 250

 300

 350

 400

 425

9 11 13 15 17 19 21 23 25 27 29

Soil Temperature (oC)

Day of Year 2002

2 cm

5 cm

10 cm

30 cm

50 cm

136.5 cm

Figure 7—Twenty days of

recorded soil temperatures (a) and

heat fluxes (b) at the slash pile site.

Time series include two days

before the fire and include 18 days

after the fire. The fire was initiated

about 12:20 PM MST on day 11

of 2002 and burned for several

hours. The manufacturers of the

soil heat flux plates used during

this experiment are given in the

figure legend.

-1800

-1600

-1400

-1200

-1000

-800

-600

-400

-200

 0

 200

 400

9 11 13 15 17 19 21 23 25 27 29

Soil Heat Flux (W m-2)

Day of Year 2002

5 cm (Thermonetics Corporation)

10 cm (Thermonetics Corporation)

30 cm (Thermonetics Corporation)

50 cm (Radiation Energy Balance Systems)

136.5 cm (Radiation Energy Balance Systems)
USDA Forest Service Proceedings RMRS-P-29. 2003. 81

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten

signal may be caused by seepage of soil moisture or water vapor through cracks

or flaws in the glass glaze that seals the heat conductor from moisture. The

directional change may also be related to the abrupt change from cooling to

warming associated with the collapse of the burn pile about 35 minutes after

initiating the fire. Both the 5 and 10 cm heat flux traces show two heat pulses

separated by a brief period during which the magnitude of the flux is diminished. Furthermore, the temperature traces also show corresponding periods

during which the rate of temperature increase slows considerably. When combined with the heat flux data these temperature data implicate, not only the

fire and pile structure dynamics, but also that some of the thermal energy may

be vaporizing any remaining soil moisture as the heat pulse propagates through

the soil. The final phase of this experiment, discussed in the next section,

included retrieving the sensors, rechecking their placement in the soil and the

calibration factors of the heat flux plates, and in general examining them for

possible defects or damage that may have occured during the periods of installation, the fire, or the site amendment.

Analysis

By using a simple mathematical description of the soil thermal pulse it is

possible to estimate (or extrapolate to) the maximum temperatures and heat

fluxes that occured at the soil surface during the fire. This information is probably the best way to standardize comparisons between the fires, but it may not

be directly measurable. It is also useful for inferring something about the initial soil moisture content and its influence on the soil thermal properties as

well as highlighting potential problems with the sensors. To accomplish this

we assume a model that (at least approximately) partitions the thermal input

to the soil into a dominant fire component and a secondary component representing all other thermal inputs to the soil. The simplest model we can assume

relates the vertical profile of the measured amplitude (or maximum) of the

heat pulse to soil depth as follows:

 Tmax(z) = T0

 + DT e -z/D (1)

and

Gmax(z) = G0

 + DG e -z/D (2)

where Tmax(z) is the maximum observed value of the soil temperature as a

function of depth, T0 is a constant temperature, DT is the temperature amplitude associated with of the thermal pulse of the fire, and D is the soil thermal

attenuation depth, which is related to the thermal properties of the soil. The

symbols Gmax(z), G0

, and DG, used for soil heat flux, have a similar interpretation. By using the model expressed by equations (1) and (2) we follow an

approach similar to Raison et al. (1986) who also suggested that the maximum fire related soil temperature decreased exponentially with soil depth.

However, the present analysis differs from their study because they did have

soil heat flux data nor did they calculate a thermal attenuation depth.

Because we assume that the fire dominates all other forms of thermal input

to the soil we should expect that DT >> T0

 and DG >> G0 are required for the

model to be valid. Therefore, as we depart from this condition the model

results will become less reliable. As a consequence, we can state a priori that

the larger and more intense the fire, the more useful this model will be for

interpretive purposes. Under this analysis scenario, DT is the maximum soil

surface temperature during the fire and DG is the maximum soil heat flux at

the surface during the fire.
82 USDA Forest Service Proceedings RMRS-P-29. 2003.

The main benefit to this method of analyzing the fire data is its simplicity.

However, one weakness is that it focuses only on the attenuation of the

thermal pulse and does not include any aspect of the phase or time lag associated with the propagation of the thermal pulse through the soil. A phase analysis,

which requires significantly more mathematical and computational effort, is

beyond the intentions of the present study and is the subject of a later study.

In this analysis we also treat temperature and soil heat flux independently,

although theoretically at any given site the thermal attenuation depth, D, should

be the same for both temperature and soil heat flux. The implications of similar and different values of D are discussed after presenting the results of the

analysis.

To apply this model and these concepts to the present set of observations

we simply fit equations (1) and (2) to the profile of observed maximum temperatures and heat fluxes. Table 3 gives the results of this analysis for the slash

pile burn and the high and medium fuel burns at the forest and meadow sites.

Note that because we are treating temperature and heat flux independently

for this analysis there are two values for D given in table 3. The first (from left

to right) is associated with temperature and the second is associated with the

heat flux. The low fuel sites, which are not good candidates for this type of

analysis, are not included in table 3 because it was not possible with such low

intensity fires to distinguish between the thermal signals of the fires and that

of the normal background. Figure 8 shows the graphs of the curve fits and

observations for the slash pile burn. For this calculation the 30 cm soil heat

flux data were not used because it was not possible to unambiguously identify

the 30 cm peak magnitude for Gmax(z). The shaded areas of these figures correspond to the model’s standard error of the estimate. Given the size and

intensity of the slash pile burn, it probably provides the most reliable results.

Overall, for most of these burns the thermal pulse was easily identified and the

results confirm the requirement that DT >> T0 and DG >> G0 . Only the temperature data at the medium fuel forest site is questionable in this regard.

Table 3 indicates that DT varied significantly from site to site. However, the

meadow site DT values appear surprisingly high, suggesting there may have

been temperature sensor problems. On the other hand, these high values may

result from the rather high burn rate associated with the meadow sites. But,

even before the burn the 2 cm temperature sensor at the medium loading

meadow site seemed to be measuring anomalously high temperatures (for

example compare figures 2a and 3a). The post fire inspection of the temperature sensors suggested that the nominal 2 cm temperature probes were actually

much nearer the surface at both the meadow sites. The cause for this sensor

misplacement is unknown, but it may have occured as a result of an

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site

Table 3—Extrapolated surface temperatures, T, and heat fluxes, G, and inferred attenuation

depths, D, for burns at the Manitou Experimental Forest. The standard error of the estimates

are enclosed in parentheses. I = insufficient number of data points to estimate standard

error of the estimate.

T0 T DG0 G D

Site (loading) (C) (C) (cm) (Wm -2) (Wm -2) (cm)

Slash pile 31 (13) 436 (21) 14.2 (2.0) 15 (44) -2350 (121) 16.9 (2.3)

Meadow (high) 24 (6) 734 (140) 1.8 (0.3) -14 (I) -2324 (I) 4.5 (I)

Meadow (med) 10 (1) 2049 (467) 0.9 (0.1) -1 (I) -454 (I) 4.7 (I)

Forest (high) 12 (2) 95 (7) 4.0 (0.5) -9 (I) -2968 (I) 3.0 (I)

Forest (med) 11 (1) 36 (4) 5.8 (1.2) -5 (I) -2001 (I) 3.4 (I)

D D

D D
USDA Forest Service Proceedings RMRS-P-29. 2003. 83

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten

Figure 8—(a) Vertical profile of

maximum measured soil

temperatures (•) and the

corresponding curve fit from

equation (1) at the slash pile burn.

(b) Vertical profile of maximum

measured soil heat fluxes (•) and

the corresponding curve fit from

equation (2) at the slash pile burn.

The shaded area in each figure

encloses the model’s standard error

of the estimate.

-1

-2

-3

-5

-10

-20

-30

-50

-100

-200

0 50 100 150 200 250 300 350 400 450 500

Depth (cm)

Soil Temperature (oC)

model, equation (1)

standard error of the estimate of the model

measured maximum temperature

-1

-2

-3

-5

-10

-20

-30

-50

-100

-200

-300 0 300 600 900 1200 1500 1800 2100 2400

Depth (cm)

Soil Heat Flux (W m-2)

model, equation (2)

standard error of the estimate of the model

measured maximum downward heat flux
84 USDA Forest Service Proceedings RMRS-P-29. 2003.

unintended disturbance during the site amendment or it may have resulted

from a measurement error when the sensors were originally installed. There

was no evidence of any misplacement or movement of the 2 cm soil heat flux

plates at the meadow site, but the soil heat flux plates are much larger and

more easily secured than the soil temperature probes. Table 4 is a recalculation of the model fits for equations (1) and (2) at the meadow site using a post

fire estimate of the depth of the sensor probes. The recalculated DT values are

still quite high at the meadow site, but they appear somewhat more reasonable. The post fire inspection also suggested that the soil sensors at the high

fuel loading site were very close to a burned clump of grass, suggesting that

this microscale feature of the fire may have contributed toward biasing these

results somewhat high. Nevertheless, the recalculated values still indicate that

there are significant site-to-site differences in soil temperatures, suggesting

the need to examine why temperatures at the grass site were so much higher

than the forest site.

These temperature differences likely resulted from differences in fuel type

and loading, moisture content, and soil thermal properties. The differences in

fuel type and loading between the meadow and forest sites (grass versus pine

needles) likely contributed to a faster rate of fire spread across the meadow

plots. These meadow burns lasted less than 3 minutes around the area where

the sensors were buried, whereas the forest burns lasted between 10 and 12

minutes and the slash pile burn lasted several hours. Therefore, the rate of

energy release was probably greatest at the meadow site. This is unquestionably true when compared with the two forest plots because of the much heavier

fuel loading at the two meadow sites (table 1). Thus the thermal pulse at the

meadow plots was probably confined to the upper few centimeters of soil,

which is supported by the fact that the 5 cm meadow data show much less of

a thermal pulse than the temperature data above 5 cm. The difference in thermal attenuation depth, D between temperature and heat flux is also greatest

at the meadow site. This may be due to greater variation with depth of the soil

thermal properties, which are functions of soil moisture and other soil physical parameters, than at the other sites. Finally, the total moisture content of

the duff and litter layers at the meadow site was higher than at the forest site.

At the meadow site the duff layer was not only deeper than the forest site

(table 1), but on the day of the fire it was 33 percent water by weight, whereas

the forest duff layer was 14 percent by weight. The same is also true for litter

layer. However, on the day of the fire the litter layer at the forest site was

slightly higher (23 percent by weight) than the litter layer at the meadow site

(15 percent by weight). But, the total mass of litter at the meadow site was

more than enough to compensate for the slightly lower water content. However, the moisture content of upper few centimeters of soil was about the same

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site

Table 4—Post fire reanalysis of extrapolated surface temperatures, T, and heat fluxes,

G, and inferred attenuation depths, D, for medium and high density fuel loadings at the

meadow site burns. Sensor depth is assumed to be 1.0 cm for the high density plot and

0.25 cm for the medium density plot. The standard error of the estimates are enclosed in

parentheses. I = insufficient number of data points to estimate standard error of the

estimate.

T0 T DG0 G D

Site (loading) (C) (C) (cm) (Wm -2) (Wm -2) (cm)

Meadow (high) 22 (6) 359 (25) 2.4 (0.4) -12 (I) -1812 (I) 5.0 (I)

Meadow (med) 10 (1) 253 (5) 1.4 (0.1) 0.2 (I) -310 (I) 5.8 (I)

D

D

D D
USDA Forest Service Proceedings RMRS-P-29. 2003. 85

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten

at both sites (between 2 percent and 3 percent by weight). Evaporating the

additional moisture in the upper soil layers at the meadow site would also have

tended to limit the thermal heat pulse to the upper few centimeters of the soil

when compared with the forest site. On the other hand, the soil moisture

content at the meadow site would have reduced soil temperatures and heat

fluxes relative to what would have occured if the site had been drier (Frandsen

and Ryan 1986).

Like the extrapolated surface temperature, DT, the extrapolated surface heat

flux, DG, also shows significant variation. In general however, except for the

medium fuel meadow site, DG was between 2000 and 3000 Wm-2 at all sites,

which is in good agreement with the observations of surface soil heat flux

made under a burning experimental log pile by Tunstall et al. (1976). Even

allowing for the (unsupported) possibility of a meadow site soil heat flux plate

positioning problem, the results of the recalculation at the meadow site (table

4) do not significantly alter this general conclusion concerning the surface

heat flux, DG. Consequently, DG at the medium fuel loading meadow plot

does appear anomalously low. The recalibration of the REBS soil heat flux

plates (table 2) indicated that all sensors were functional and that they had

maintained their calibrations in spite of the high temperatures. Possible explanations of the relatively low value for DG at the medium loading meadow site

are poor contact between the soil and the 2 cm soil heat flux plate or the

microstructure of the fire on this plot was such that the soil just above the

2 cm heat flux plate was not exposed to as much heat as the temperature

sensors, which were separated laterally from the heat flux plates by 6-8 cm.

Poor contact seems more likely, although we cannot completely eliminate the

other possibility. The recalibration of the Thermonetics sensors also showed

that they survived the fire at the slash pile site without damage, which reinforces our confidence that all other DG values are reasonable and that value for

DG at the medium loading meadow site is in fact related to measurement

error.

Although we did not list it in table 2, one of the REBS sensors was destroyed (melted) during the slash pile burn. We had installed this particular

sensor at a depth of 10 cm before the fire to compare with the 10 cm

Thermonetics sensor. However, the data obtained by this sensor before the

fire were not consistent with the 10 cm Thermonetics sensor or any of the

other 10 cm sensors. An examination of this sensor after the fire showed that

the shrink tubing at the splice joining the sensor wires to the extension wires

was not in good contact with the splice, which could have allowed moisture to

short the connection and degrade the sensor signal. A similar problem was

found with the heat flux plate at the 2 cm low loading meadow plot (figure

1b), which in this case may have caused an underestimation of the soil heat

flux at 2 cm. (Otherwise the soil heat flux wave at 2 cm would show more

attenuation at 10 cm than it does; see figure 1b.) Again, however, we cannot

rule out poor contact as the cause for these two problem sensors. Nevertheless, potential measurement problems before the fire would not have caused

the sensor to melt. For the benefit of future studies we note that the maximum soil temperature measured during the fire at this site was 232 C and that

this REBS sensor was exposed to temperatures in excess of 150 C for over 48

hours (figure 7a). The 2 cm heat flux plates at the meadow medium and high

loading plots were exposed to similar temperatures, but their exposure times

were much shorter (figures. 2a and 3a).

Finally tables 3 and 4 also suggest that the attenuation depth, D, varies

significantly from site to site. The large differences in the values of D between

the slash burn site and the other sites suggests either significantly different soil
86 USDA Forest Service Proceedings RMRS-P-29. 2003.

thermal properties at the slash burn site or that the duration and intensity of

the slash burn fire were sufficient to alter the soil thermal properties by evaporating the soil moisture at this site, combusting soil organic matter, and altering

soil physical structure (e.g., DeBano et al. 1998). Evaporating soil

moisture, at least, is quite likely if the soil contained any water in the upper

30 - 50 cm at the time of the fire. The significant variation in D between the

temperature data and the soil heat flux data at the meadow site is probably

more indicative of some of the previously discussed problems with the temperature and heat flux sensors at this site than it is indicative of any soil physical

or thermal characteristics of the site.

Discussion and Conclusions

This study has demonstrated that it is possible to measure soil heat flux

during a surface burn. Such data are important for modeling the soil thermal

pulse during fires and to help with diagnosing possible soil moisture effects

and evaporation during fires. Present results also confirm that soil temperatures and heat fluxes associated with surface fires can vary significantly with

fuel loading, duration and intensity of the fire, and (at least indirectly) with

the moisture content of the surface litter and duff. This study has also shown

that large, long duration fires in heavy surface fuels can cause the thermal

pulse to reach deeper into the soil and are more likely to affect belowground

biota than shorter and more intense fires. Steward et al. 1990 reached a similar conclusion in their modeling study of surface fires. In addition, results of

this study also suggest that surface temperatures of shorter more intense fires

may also be high, but that the effects may be confined to shallower soil depths.

It is possible, therefore, that such fires may cause greater loss of seeds lying on

or embedded shallowly within the soil than larger less intense fires. This last

scenario depends of course on the individual and species seeds’ heat resistance

and the moisture content of the near-surface soil layers, with the more moist

soils associated with lower lethal temperatures (DeBano et al. 1998). Nevertheless, during this experiment soil temperatures at all but the lightest fuel

loading plots reached nearly 100 C which should prove lethal to virtually all

seeds (Hungerford et al. 1991, DeBano et al. 1998).

Management implications of these results for the Front Range of Colorado

would indicate that burning surface fuel loadings under ponderosa pine forests and those resulting from lopping and scattering logging slash tend to

produce soil heat pulses that are of relatively short duration and have a shallow depth of penetration. Burning large slash piles produce long duration,

high temperature heat pulses that penetrate deep into the soil, potentially

altering both physical and biotic characteristics of the soil to significant depths.

Potential consequences of such large intense fires include the increased risk of

erosion and a decreasing likelihood of seeding establishment and survival

(DeBano et al. 1998).

References

Campbell, G.S., Jungbauer, J.D., Jr., Bristow, K.L., Hungerford, R.D. 1995. Soil

temperature and water content beneath a surface fire. Soil Science. 159: 363-374.

Massman, Frank, Shepperd, and Platten In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site
USDA Forest Service Proceedings RMRS-P-29. 2003. 87

DeBano, L.F., Neary, D.G., Ffoilliot, P.F. 1998. Fire’s Effects on Ecosystems. New

York: John Wiley & Sons. 333 p.

Frandsen, W.H. and Ryan, K.C. 1986. Soil moisture reduces belowground heat flux

and soil temperatures under a burning fuel pile. Canadian Journal of Forest Research.

16: 244-248.

Hungerford, R. D.; Harrington, M. G.; Frandsen, W. H.; Ryan, K. C.; Niehoff, G. J.

1991. Influence of fire on factors that affect site productivity. In Proceedings of

the management and productivity of western-Montane forest soils. A.E. Harvey

and L. F. Neuenschwander (Eds.). USDA Forest Service, Intermountain Research

Station General Technical Report INT-GTR-280.

Raison, R. J.; Woods, P. V.; Jakobsen, B. F.; Bary, G. A. V. 1986. Soil temperatures

during and following low-intensity prescribed burning in a Eucalyptus pauciflora

forest. Australian Journal of Soil Research. 24: 33-47.

Sackett, S. S.; Haase, S. M. 1992. Measuring soil and tree temperatures during

prescribed fires with thermocouple probes. USDA Forest Service, Pacific Southwest

Research Station General Technical Report PSW-GTR-131.

Steward, F.R., Peter, S., Richon, J.B. 1990. A method for predicting the depth of

lethal heat penetration into mineral soils exposed to fires of various intensities.

Canadian Journal of Forest Research. 20: 919-926.

Tunstall, B. R.; Martin, T.; Walker, J.; Gill A. M.; Aston, A. 1976. Soil temperatures

induced by an experimental log pile fire: Preliminary data analysis. CSIRO Division

of Land Use Research, Technical Memorandum 76/20.

USDA; Forest Service; Soil Conservation Service; Colorado Agricultural Experiment

Station. 1986. Soil Survey of Pike National Forest, Eastern Part, Colorado, Parts

of Douglas, El Paso, Jefferson, and Teller Counties.

In Situ Soil Temperature and Heat Flux Measurements During Controlled Surface Burns at a Southern Colorado Forest Site Massman, Frank, Shepperd, and Platten
88 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 89

Restoration Case Studies

and Ecosystem Effects
90 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 91

Ecological Restoration Thinning of Ponderosa

Pine Ecosystems: Alternative Treatment

Outcomes Vary Widely

W. Wallace Covington1

Ecological restoration prescriptions should be designed using the best

available scientific knowledge, have their likely consequences analyzed,

and have their consequences monitored. To illustrate these points, three levels of ecological restoration thinning were analyzed for outcomes for a

permanent plot established in 1909 on the Fort Valley Experimental Forest.

This plot had 47.4 trees per acre in 1876 and 383 in 1997. Simulation modeling suggests that a strict restoration to 1876 densities would produce stand

conditions that would support only surface burning with no crown scorch

and release 491 btu/ft2

. Leaving three times as many trees would produce

stand conditions that would support passive crown fire with 70 percent crown

scorch and release 1800 btu/ft2

. Beyond fire behavior, predicted resource

values vary widely as well. Strict restoration would produce a stand with high

near view scenic quality (Scenic Beauty Indicator of 85), high grass and wildflower production (856 lbs/acre), and relatively high water yield (6.7 inches).

The three fold level would produce low scenic quality (SBI 34), lower herbaceous production (134 lbs/acre), and lower water yield (6.1 inches). At its

best, ecological restoration can improve ecosystem health, reduce unwanted

fire behavior, and enhance human resource values. At its worst, it could be a

waste of effort and resources.

1Ecological Restoration Institute,

Northern Arizona University, Flagstaff,

AZ.
92 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 93

Aspen Response to Prescribed Fire, Mechanical

Treatments, and Ungulate Herbivory

Steve Kilpatrick1, Dean Clause2, Dave Scott3

Abstract—Land management agencies in northwestern Wyoming have implemented

vegetation treatment programs to stimulate aspen (Populus tremuloides) regeneration. Treated clones are susceptible to extensive browsing from elk (Cervus elaphus)

concentrated on adjacent supplemental feedgrounds, wintering moose (Alces alces

shirasi), and livestock. We sampled eight treated (mechanical cutting and prescribed

fire) aspen clones (stands) to determine treatment response 3-9 years post-treatment.

A sampling design was tested for monitoring pre- and post-treatment stem densities.

Total aspen sucker densities ranged from 3,480 to 29,688 stems/ac (8,600 to 73,360

stems/ha). Two 9-year-old treatments and one 7-year-old treatment achieved > 1,000

stems > 10 ft in height /acre (> 2,710 stems > 3.1 m/ha), the objective for successful

clone reestablishment. Mean annual leader growth was 7.2 inches (18.3 cm) and

ranged from 4.9 to 12.9 inches (12.4 to 32.8 cm). Treated clones are all expected to

reestablish successfully. Stem density, clone homogeneity, and plot size influenced

sampling efficiency.

Introduction

Aspen (Populus tremuloides) is found throughout Wyoming’s major

mountain ranges. The larger stands occur in the Sierra Madre, Wind

River, and Gros Ventre mountain ranges (Merrill et al. 1996). Total acreage in

Wyoming is estimated at 338,000 acres (Green and Van Hooser 1983). However, it is also estimated the historical acreage (100–150 years previous) was at

least double the present. The successional replacement of aspen with conifers,

shrubs and herbaceous vegetation continues today (Debyle and Winokur 1985;

Bartos and Campbell 1998). Factors contributing to aspen decline and lack of

regeneration include fire suppression, livestock grazing, wild ungulate browsing, and natural succession (Krebill 1972; Bartos and Campbell 1998; Gruell

and Loope 1974; Mueggler 1989; Romme et al. 1995).

Aspen communities are recognized for their multiple values, including recreation, scenic vistas, water yield, water quality, wood products, habitat for an

array of wildlife species, forage for wild and domestic ungulates, and landscape diversity (Bartos and Campbell 1998; DeByle and Winokur 1985). A

minimum of 140 mammal and bird species utilize aspen habitat types in Wyoming (Dieni and Anderson 1997). The decline of aspen communities in the

West, and throughout the state of Wyoming, is a concern for ecologists and

resource managers. Recently, fire managers have also become concerned with

the loss of the “asbestos forest type” (Fechner and Barrows 1976). Healthy

aspen stands provide natural firebreaks which reduce fire intensity and severity, allowing fire managers additional control options.

Successful aspen regeneration in the West occurs almost exclusively through

vegetative propagation (Debyle and Winokur 1985). Reproduction is clonal

1Habitat Biologist, Wyoming Game &

Fish Department, Jackson, WY.

2Biologist, Wyoming Game & Fish

Department, Pinedale, WY.

3Fire Ecologist, US Forest Service,

Bridger-Teton National Forest, Jackson,

WY.
94 USDA Forest Service Proceedings RMRS-P-29. 2003.

in nature (Barnes 1966) and generally enhanced with disturbances that kill

overstory trees and preclude auxin transfer to the roots, thus stimulating

suckering (Debyle and Winokur 1985). Management activities that kill or

stress overstory trees (e.g., prescribed burning, clear-cutting, herbicide treatments) mimic natural disturbances and enhance aspen regeneration.

The appropriateness of implementing such management actions in areas of

intense ungulate herbivory has been questioned. Aspen enhancement projects

located near supplemental elk feeding sites (feedgrounds) in northwest Wyoming have resulted in varying levels of success. Krebill (1972) concluded that

natural aspen recruitment of 653 stems/ac (1,614 stems/ha) in the Gros Ventre

was not sufficient to replace overstory mortality given impacts from herbivory.

Hart (2000) re-examined aspen stands evaluated by Krebill and concluded

that “herbivory and disease, superimposed on successional events, may be

exerting negative effects on the distribution of aspen.” Bartos et al. (1994)

concluded that a prescribed burn treatment of aspen near elk (Cervus elaphus)

feedgrounds in the Gros Ventre drainage may have hastened the demise of

decadent aspen clones. However, other prescribed burns in the same area that

lacked intense herbivory were successful (Bartos et al. 1991). Dieni et al. (2000)

concluded that aspen regeneration through clear-cutting on the U.S. Fish and

Wildlife Service’s National Elk Refuge may have hastened their demise as a

result of excessive elk herbivory. Kilpatrick and Abendroth (2000) emphasize

aspen treatment site selection. They suggest the following factors, alone or in

combination, may contribute to outcome following aspen treatment: site aspect, site distance from concentrations of wintering ungulates and elk

feedgrounds, supplemental elk feeding regime, aspen community type, stand

vigor, soil type, fire intensity/severity, and level of human disturbance to wildlife.

In this current study, our primary objective was to quantify aspen regeneration post-treatment (mechanical clearing and prescribed burn) at two different

areas in northwest Wyoming. Eight post-treatment sites were sampled within

the two areas. Both areas are near supplemental elk feedgrounds where herbivory levels could potentially impede aspen regeneration. A secondary objective

was to test the accuracy and efficiency of our aspen sampling methodology.

Study Area

This study deals with aspen sampling at eight post-treatment and five

pre-treatment sites located south and east of Jackson, Wyoming (tables 1

and 3).

Two post-treatment sites were sampled at the Bryon Flats area located on

the Bridger-Teton National Forest approximately 6 miles (9.6 km) southeast

of Hoback Junction, Wyoming. Sites are on the east side of Willow Creek, a

tributary of the Hoback River. One site was clear-cut in 1994 and the other

prescribed burned during the fall of 1995. General treatment goals were to

reduce conifer densities, promote aspen suckering, and set back succession.

The Wyoming Game & Fish Department operates the Camp Creek supplemental elk feedground approximately 2 miles northwest of the treated sites

where 600-1000 elk are fed baled hay during the winter months (December –

April). A relatively small moose (Alces alces shirasi) population utilized the

area rear-round. Mule deer (Odocoileus hemionus) utilized the area during the

spring – fall. The sites also received summer cattle grazing.

Six post-treatment aspen sites were sampled during 2000 at the Soda Lake

site, approximately 7 miles (11.3 km) north of Pinedale, Wyoming. The

Kilpatrick, Clause, and Scott Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory
USDA Forest Service Proceedings RMRS-P-29. 2003. 95

Wyoming Game and Fish Department operates an elk supplemental feedground

near Soda Lake. Approximately 800-1,000 elk were historically fed on the

north side of Soda Lake until 1993, 1-2 miles (1.6-3.2 km) from the monitored sites. After 1993 the feeding site was relocated south of Soda Lake,

which extended the distance to the sampled sites 2-4 miles (3.2-6.4 km). Moose

utilized the area year-round and mule deer inhabited the area during spring

and fall.

Five pre-treatment aspen sites were sampled during 2001, at the Fremont/

Pinyon Ridge site near Pinedale. Two of the sites were located on the north

end of Fremont Ridge, approximately 7 miles (11.3 km) north of Pinedale.

The remaining three sites were located on Pinyon Ridge, approximately

35 miles (56 km) north of Pinedale.

Methods

A pilot sampling methodology was utilized to evaluate efficiency in acquiring sufficient samples to provide sucker density data sets with 80% confidence

and 20% error. These statistical parameters were recommended to land managers for monitoring the success of treatments designed to enhance aspen

regeneration (Winward et al. 2000). In addition to density, height and annual

leader growth also were monitored. Sampling steps for our study reported

here were (see also figure 1):

• Randomly establish a permanent photo point with a 5 ft steel post near

the center of the clone/stand. Record GPS coordinates.

• Take photos in the four cardinal directions.

• Select a random azimuth from the permanent photo point.

• Select a random pace distance.

Table 1—Aspen post-treatment site location, code, date, herbivory and elevation.

Post-treatment site location and type Code Treatment date Herbivory Elevation (ft)

Soda Lake - WGFD Rx burn SLGFBurn Fall 1991 elk, moose, deer 7690

Soda Lake - Forest Service Rx burn SLFSBurn Fall 1991 elk, moose, deer, cattle 7850

Soda Lake - WYGFD mechanical cutting SLGFCut Sum. 1991 elk, moose, deer 7500

Soda Lake - Forest Service Exclosure -Rx burn SLEXBurn Fall 1991 none 7770

Soda Lake - Spring Creek FS Rx Burn SLSCBurn Fall 1991 elk, moose, deer, cattle 8345

Burnt Lake - FS mechanical cutting BLFSCut Fall 1997 elk, moose, deer, cattle 8040

Willow Cr. Rx burn WCBurn Fall 1995 elk, moose, deer, cattle 6808

Willow Cr. Mechanical Cutting WCCut Fall 1994 elk, moose, deer, cattle 6888

Table 2—Post-treatment sampling

comparisons, of plot size, number of

plots, and stem densities (80%

confidence, 20% error).

Plot size No. plots Stems/ac

1/500 57 4865

1/500 29 5414

1/500 15 15133

1/500 6 10100

1/500 5 29688

1/500 17 6367

1/300 15 3480

1/100 23 4039

Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory Kilpatrick, Clause, and Scott
96 USDA Forest Service Proceedings RMRS-P-29. 2003.

• Select circular plot size. Recommend 1/50 to 1/100 acre (1/124-

1/247 ha) for pre-treatment mature tree densities estimated at 150-250

trees/acre (370-618 trees/ha). Recommend 1/100-1/500 acre (1/247-

1/1,236 ha) plot size for post-treatment densities of 4,000-15,000 stems/

acre, (9,884-37,065 stems/ha) respectively. Also, increase the plot size

with increased stand/clone heterogeneity.

• Proceed along random azimuth from permanent photo point sampling

plots at the random pace intervals. Record within plots: (a) height of

stems by 1 ft class (e.g., <1, 1-2, ...>10)/plot, (b) number of stems/

plot, (c) annual leader growth on two to three dominant leaders of random suckers/plot.

• Stop proceeding along random azimuth when a different community

type or ecotone is encountered.

• Select a new random azimuth that intercepts the stand/clone, and select

a new random pace.

• Continue the above procedure until the required sample size and statistical reliability is achieved using the following formula (e.g., 80% reliability

with 20% error).

 N = (t)2 * (s)2 / (P * M)2

 N = required sample size

 t = t table value for desired confidence level (e.g., 80% , 90% C.I.)

 s = standard deviation

 P = percent error (e.g., 20% = 0.20)

 M = mean # stems/plot

Bartos and Winward (2000) recommend the following post-treatment conditions for successful aspen clone reestablishment: >1,000 stems/acre (2,471

stems/ha), >10 ft (3.1 m) in height within 10 years post-treatment. They also

suggest mean sucker height should increase by 1-ft/year (0.31 m/year)

Aspen Sampling Design

Aspen Clone

or Stand

Random

Azimuth

Photo pt.

Circular Plots

at random

paces/azimuth

Figure 1—Sampling design used for

estimating pre- and post-treatment

aspen stem density, height, and

current year leader growth.

Kilpatrick, Clause, and Scott Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory
USDA Forest Service Proceedings RMRS-P-29. 2003. 97

post-treatment. The above recommendations were transformed into objectives for our aspen treatments.

Areas of mixed-aged aspen and conifers selected for this study were considered to be “at risk” as described by Bartos and Campbell (1998) and were

sampled with the above detailed procedure.

Results

Three of the treated sites, SLSCBurn, SLEXBurn, and WCCut, achieved

means of >1,000 stems >10 ft in height/acre (>2,471 >3.1 m stems/ha) (figure 2). The fall 1991 Soda Lake Spring Creek prescribed burn (2,200 >10 ft

stems/acre; 10,100 mean total stems/acre; figures 2 and 3) was located at the

highest elevation and greatest distance from the Soda Lake elk supplemental

feedground near Pinedale, Wyoming. Elk herbivory appears to be light during fall and early winter when elk are migrating through the site towards the

feedground. Snow depths and human disturbance (winter recreation) are suspected to preclude heavier elk herbivory levels. Cattle use of this site is also

considered to be light or moderate during the growing season.

500 325

2200

1700

233

1500

466

0 0

1000

2000

3000

4000

5000

6000

Treatment

Stems/acre

SLGFBurn(F91)

SLFSBurn(F91)

SLSCBurn(F91)

SLEXBurn(F91)

WCBurn(95)

WCCut(94)

SLGFCut(F91)

BLFSCut(97)

4039 4865

10100

6367 3480 5413

15133

29688

0

5000

10000

15000

20000

25000

30000

35000

40000

Treatments

Stems/acre

SLGFBurn(F91)

SLFSBurn(F91)

SLSCBurn(F91)

SLEXBurn(F91)

WCBurn(95)

WCCut(94)

SLGFCut(F91)

BLFSCut(F97)

Figure 2—Mean and one

standard deviation for stem

densities of aspen suckers >10 ft

in height at time of sampling.

Figure 3—Mean total aspen stems per

acre by treatment. Error bars indicate

P = .8 confidence interval.

Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory Kilpatrick, Clause, and Scott
98 USDA Forest Service Proceedings RMRS-P-29. 2003.

The fall 1991 Soda Lake exclosure prescribed burn (SLEXBurn), located

near Pinedale, Wyoming, was the second site appearing to meet the regeneration objectives (figure 2). It is situated at a lower elevation than the above site

and is adjacent to the boundary between the Wyoming Game and Fish Department and the USFS. The 8-ft fence surrounding the exclosure was elk and

livestock-proof, excluding all herbivory. Mean stems >10 ft (3.1 m) in height

and mean total stems were 1,760 and 10,133 stems/acre (4,349 and 25,038

stems/ha) respectively.

The third site having greater mean sucker density and height was the

Willow Creek mechanical cutting treatment (WCCut), located near Jackson,

Wyoming. It received light wild ungulate herbivory despite being within

2 miles (3.2 km) of a supplemental feedground. Possible reasons for the light

herbivory use were location of the site away from a major elk migration route

to the Camp Creek supplemental elk feedground and human disturbance from

winter recreation. Moose are thought to be responsible for most of the herbivory occurring on the site. Moose are more tolerant of human disturbance

than elk and their droppings were evident at the site. Mean total aspen stems

and those >10 ft in height were 4,480 and 1,700 stems/ac (11,070 and 4,200

stems/ha) respectively (figures 2 and 3).

The remainder of the treatments with >7 growing seasons had total mean

sucker densities ranging from 4,039 to 6,367 stems/ac (9,980-75,732 stems/

ha). The Burnt Lake Forest Service mechanical cutting site had 29,688 stems/

ac (73,359 stems/ha) after its third growing season. All densities were within

the 850-19,951 stems/ac (2,100-49,300 stems/ha) reported by others (Bartos

et al. 1994; Patton and Avant 1970; Brown and DeByle 1987; Brown and

DeByle 1989; Bartos et al. 1991; Kilpatrick and Abendroth 2000).

The median height class of aspen suckers nine years post-treatment ranged

from 4-5 ft (1.2-1.5 m) to 5-6 ft (1.5-1.8 m) (figure 5). The median height

classes for the Willow Creek burn (six years post-treatment) and mechanical

cutting (seven years post-treatment) were 5-6 ft (1.5-1.8 m) and 6-7 ft

(1.8-2.2 m) respectively. Median height class for the Burnt Lake mechanical

cutting was 1-2 ft (0.3-0.6 m), three years post-treatment.

Current annual growth of dominant leaders did not meet the management

objective of 12 inches/year (30.5 cm/year). Mean annual leader growth rate

ranged from 4.9-12.9 inches (12.4-32.8 cm) and averaged 7.2 inches (18.3 cm)

across all treatments (figure 4). Bartos et al. (1991) documented similar results with average sucker heights increasing 0.8-8.6 inches/year (2.0-22.0

cm/yr) on burned sites near Jackson, Wyoming.

The pre- and post-treatment sample size required to meet the statistical

objective of 80% confidence + 20% error ranged from 15-29 and 5-57 plots,

respectively (tables 2 and 3). Stem density, homogeneity of the sampled clone/

stand, and plot size influenced the minimum sample size.

0

5

10

15

Treatment

Mean = 7.2"

Inches

SLGFBurn

SLFSBurn

SLGFCut

SLSCBurn

BLFSCut

SLEXBurn

WCBurn

WCCut

Figure 4—Current annual growth of

dominant aspen leaders by treatment

site (black bars). Blue bars represent

the mean.

Kilpatrick, Clause, and Scott Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory
USDA Forest Service Proceedings RMRS-P-29. 2003. 99

BLCut - 3 yr Post-Treatment

19

42

20

11

4 2 1 0 000

0

10

20

30

40

50

Height Class (ft)

WCBurn - 6 yr Post-Treatment

0 0

3

13

21 22

17 17

0

6

4

0

5

10

15

20

25

Height Class (ft)

WCCut - 7 yr Post-Treatment

4 5 9

16 11 7

1 1 0 3

43

0

10

20

30

40

50

Height Class (ft

SLGFBurn - 9 yr Post-Treatment

0 2

8

15 13 13 11 11

8 6

12

0

5

10

15

20

Height Class (ft)

SLGFCut - 9 yr Post-Treatment

1

10

17 17

13

9 8 6 5 4

10

0

5

10

15

20

Height Class (ft)

SLEXBurn - 9 yr Post-Treatment

0 2

17 18

13 14

9

6 6 6

11

0

5

10

15

20

Height Class (ft)

SLSCBurn - 9 yr Post-Treatment

0

7

13 12 12

7 7 5 6 7

22

0

5

10

15

20

25

Height Class (ft)

SLFSBurn - 9 yr Post-Treatment

0

6

15

18 17

13

10

7 5 47

0

5

10

15

20

Height Class (ft)

Percent Stems

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10 <1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10 Percent Stems

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

Percent Stems Percent Stems Percent Stems

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

Percent Stems

Percent Stems

Percent Stems

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 >10

Figure 5—Height of aspen suckers 3-9 years post-treatment at eight different sites in Wyoming.

Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory Kilpatrick, Clause, and Scott
100 USDA Forest Service Proceedings RMRS-P-29. 2003.

Discussion

Successful management-induced aspen regeneration is quite variable within

some areas of the West. Resource managers have attempted to enhance aspen

regeneration in northwestern Wyoming since the early 1970s. Many factors

such as clone vigor, community type, fire intensity/severity, herbivory by wild

and domestic ungulates, aspect, elevation, soil type, and moisture regimes

have influenced their success. In northwest Wyoming, 25,000 elk are fed supplemental winter rations at 23 different locations. Thus, elk herbivory alone can

significantly influence aspen regeneration near these feeding locations. In some

areas, successful regeneration cannot be accomplished without clone or stand

protection with fencing. Other aspen treatment sites, such as the ones monitored in this study, appear to have promise. Continued monitoring, short and

long-term, will help managers identify factors influencing the success of treatments and make appropriate adjustments.

Two of the nine-year (SLSCBurn, SLEXBurn) and one of the seven-year

(WCCut) post-treatment sites appear as though they are approaching 10-year

post-treatment regeneration objectives. Three of the nine-year post treatment

sites (SLFSBurn, SLGFBurn, SLGFCut) may take longer than 10 years to

reach proposed objectives of >1,000 >10 ft stems/acre (>2,710 stems

>3.1 m/ha). The six-year (WCBurn) and three-year (BLCut) post treatment

sites appear to be regenerating successfully but will need continued monitoring prior to making conclusions. All eight treated sites have maintained more

than adequate sucker densities for successful clone reestablishment.

Dominant leader growth was only slightly more than half of the management objective. Severe drought conditions during 2000 and 2001 may have

resulted in reduced leader growth. Few clones appear to be averaging 1 ft

growth per year despite light to moderate ungulate herbivory. Continued

monitoring over the next two to four years is recommended to further assess

drought effects and evaluate management objectives for successful clone reestablishment.

Both wild and domestic herbivory continue to be an important factor in

clone reestablishment. Three of the sites sampled (SLEXBurn, SLGFBurn,

and SLFSBurn), were close to each other but received different levels of herbivory. The SLEXBurn was fenced and received no wild or domestic ungulate

herbivory. It had the greatest sucker stem density and height of the three sites.

The SLGFBurn received only wild ungulate herbivory and was intermediate

in density and height. Moving the Soda Lake supplemental elk feedground an

additional 1.5 miles (2.4 km) from this treatment site in 1993 appears to have

reduced elk herbivory levels on aspen. The SLFSBurn received both domestic

Table 3—Pre-treatment sampling comparisons of plot size, number of plots, and

stem densities (80% confidence, 20%

error).

Plot size No. plots Stems/ac

1/100 ac 15 1342

1/100 ac 18 1749

1/100 ac 17 206

1/100 ac 22 255

1/50 ac 29 160

Kilpatrick, Clause, and Scott Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory
USDA Forest Service Proceedings RMRS-P-29. 2003. 101

and wild ungulate herbivory and had the lowest relative density and sucker

height.

Developing an efficient, repeatable, and statistically reliable sampling methodology is important for evaluation of aspen regeneration efforts. The

methodology developed in this study to monitor aspen density and height

within pre- and post-treatment sites worked well. However, for non-randomly

distributed individuals, frequency and density estimates are affected by plot

size (Bonham 1989), and an alternative would be to adopt a standard plot size

and vary the number of plots to address heterogeneity in particular stands.

Also, we will in the future not vary the pace length of each random transect

segment since this varies the sampling intensity in different parts of the stand,

but instead maintain a uniform pace length. Development of a photo key for

estimating pre- and post-treatment stem densities would help in selecting an

appropriate plot size for each stand.

References

Barnes, B.V. 1966. The clonal growth habit of American aspens. Ecology. 47:

439-447.

Bartos, D.L.; Brown, J.K.; Booth, G.D. 1994. Twelve years biomass response in

aspen communities following fire. Journal of Range Management. 47(1): 79-83.

Bartos, D.L.; Campbell, R.B. 1998. Decline in quaking aspen in the Interior West—

examples from Utah. Rangelands. 20(1): 17-24.

Bartos, D.L.; Mueggler, W.F.; Campbell, R.B. 1991. Regeneration of aspen by

suckering on burned sites in western Wyoming. Res. Pap. INT-448. Ogden, UT:

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

10 p.

Bartos, D.L.; Winward, A. 2000. Personal communications.

Brown, J.K.; Debyle, N.V. 1987. Fire damage, mortality, and suckering in aspen.

Canadian Journal of Forest Research. 17: 1100-1109.

Brown, J.K.; DeByle, N.V. 1989. Effects of prescribed fire on biomass and plant

succession in western aspen. Res. Pap. INT-412. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Research Station.

Debyle, N.V.; Winokur, R.P., tech. eds. 1985. Aspen: ecology and management in

the Western United States. Gen. Tech. Rep. RM-119. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky Mountain Forest and Range

Experiment Station.

Dieni, J.S.; Anderson, S.H. 1997. Ecology and management of aspen forests in

Wyoming; literature review and bibliography. Wyoming Cooperative Fish and

Wildlife Research Unit, University of Wyoming, Laramie. 123 p.

Dieni, J.S.; Smith, B.L.; Rogers, R.L.; Anderson, S.H. 2000. Effects of ungulate

browsing on aspen regeneration in northwestern Wyoming. Intermountain Journal

of Sciences. 6(1): 49-55.

Fechner, G.H.; Barrows, H.S. 1976. Aspen stands as wildfire fuel breaks. Eisenhower

Consortium Bulletin 4. Fort Collins, CO: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Forest and Range Experiment Station. 26 p.

Green, A.W.; Van Hooser D.D. 1983. Forest resources of the Rocky Mountain States.

Res. Bull. INT-33. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station. 127 p.

Gruell, G.E.; Loope, L.L. 1974. Relationship among aspen, fire, and ungulate browsing

in Jackson Hole, Wyoming. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Region. 33 p.

Aspen Response to Prescribed Fire, Mechanical Treatments, and Ungulate Herbivory Kilpatrick, Clause, and Scott
102 USDA Forest Service Proceedings RMRS-P-29. 2003.

Hart, J.H. 2000. Interaction between elk, fungi, and aspen in northwest Wyoming.

In: Shepperd, Wayne D.; Binkley, Dan; Bartos, Dale L.; Stohlgren, Thomas J.

Sustaining aspen in western landscapes: symposium proceedings; 2000 June

13-15; Grand Junction, CO. Proc. RMRS-P-18. Fort Collins, CO: U.S. Department

of Agriculture, Forest Service, Rocky Mountain Research Station: 197-205.

Kilpatrick, S.A.; Abendroth, D.C. 2000. Aspen response to prescribed fire and ungulate

hervivory. In: Shepperd, Wayne D.; Binkley, Dan; Bartos, Dale L.; Stohlgren,

Thomas J. Sustaining aspen in western landscapes: symposium proceedings; 2000

June 13–15; Grand Junction, CO. Proc. RMRS-P-18. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky Mountain Research Station:

387-394.

Krebill, R.G. 1972. Mortality of aspen on the Gros Ventre elk winter range . Res.

Pap. INT-129. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Forest and Range Research Station. 16 p.

Merrill, E.H.; Kohley, T.W.; Herdendorf, M.E.; Reiners, W.A.; Driese, K.L.; Marrs

R.W.; Anderson, S.H. 1996. GAP analysis: a biological information system.

Cooperative Agreement No. 14-16-0009-1542. Laramie: University of Wyoming,

Cooperative Fisheries and Wildlife Research Unit.

Mueggler, W.F. 1989. Age distribution and reproduction of Intermountain aspen

stands. Western Journal of Applied Forestry. 4: 41-45.

Patton, D.R.; Avant, H.D. 1970. Fire stimulated aspen sprouting in a spruce-fire

forest in New Mexico. Res. Note RM-159. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station.

Romme, W.H.; Turner, M.G.; Wallace, L.L.; Walker, J.S. 1995. Aspen, elk, and fire

in northern Yellowstone National Park. Ecology. 76(7): 2097-2106.

Winward, A.; Bartos, D.L.; Shepperd, W.D. 2000. Personal communications.
USDA Forest Service Proceedings RMRS-P-29. 2003. 103

Experimental Thinning and Burning of

Ponderosa Pine Forests in Southwestern

Colorado: Effects on Canopy Structure,

Understory Composition, and Fuels

W.H. Romme1, M.L. Floyd-Hanna2, D.D. Hanna2, and Phil Kemp3

Seven 100-acre stands were treated experimentally by (1) harvesting

volume in excess of ca. 50 square feet/acre, leaving mostly larger individuals aggregated in clumps, (2) prescribed burning in spring or fall, and (3)

monitoring and assessment of treatment results. Mean tree diameter increased

from 14 cm to 29 cm. Lower crown height increased from 4.5 m to 6.6 m.

Small woody fuels (1-hr and 10-hr TL) increased with harvest but were

reduced to pre-treatment levels (mean 0.07 and 0.92 tons/acre) after burning. Larger fuels (100-hr) remained elevated (mean 3.3 tons/acre) even after

burning. Mean herbaceous cover increased from 22 percent to 32 percent and

species richness increased. Overall, the restoration treatments succeeded in

(1) reducing canopy density and increasing average tree size, (2) reducing the

hazard of crown fire and insect outbreak, and (3) rejuvenating the suppressed

herbaceous stratum. The treatments failed to (1) reduce dead woody fuel loads

on the forest floor, and (2) stimulate pine seedling establishment.

1Department of Forest Sciences, Colorado

State University, Fort Collins, CO.

2Environmental Studies Program,

Prescott College, Prescott, AZ.

3

San Juan National Forest, Durango, CO.
104 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 105

Effects of Fire Interval Restoration on Carbon

and Nitrogen in Sedimentary- and VolcanicDerived Soils of the Mogollon Rim, Arizona

Daniel G. Neary1, Steven T. Overby2, and Sally M. Haase3

Abstract—Prescribed fire was returned into over-stocked ponderosa pine stands on

the Mogollon Rim of Arizona for the purpose of restoring fire into the ecosystem and

removing fuel buildups. Prescribed fires have been ignited at intervals of 1, 2, 4, 6,

8, and 10 years to determine the best fire return interval for Southwestern ponderosa

pine ecosystems. Two sites were treated: one on volcanic-derived soils, and the

other on sedimentary-derived soils near Flagstaff, Arizona, starting in 1976 and 1977

respectively. Samples from upper 5 cm of the A horizons were analyzed for total

carbon and nitrogen using an elemental analyzer. Soil carbon and nitrogen levels

were highly variable and exhibited an increasing, but inconsistent, concentration

trend related to burn interval. High spatial variability measured within treatments is

probably due to micro-site differences (location of samples in the open, under large

old-growth trees, in small-diameter thickets, in pole-sized stands, next to downed

logs, etc.). Stratification of samples by micro-site differences could possibly reduce

the within-plot variability but add considerable complexity to the sampling design.

Introduction

The pre-European settlement ponderosa pine forests of the Mogollon

Rim consisted of open stands of uneven-aged trees with a significant

grass-forb understory. Light surface-fires occurred on an average interval of

2 to 12 years in Arizona and New Mexico (Weaver 1951, Cooper 1960, Dietrich

1980). These fires consumed forest floor material, burned most of the young

regeneration, and promoted growth of a dense, grassy understory. Catastrophic

crown fires were rare due to the lack of ladder fuels and the clumpy, widely

spaced ponderosa pine canopy (Dieterich 1980, Sackett 1980). Fine fuels reduction from heavy sheep and cattle grazing and then modern forest fire

suppression resulted in the development of dense, overstocked stands.

Forest floor fuel loads that were 0.4-4.5 Mg/ha prior to 1870 have since

increased by one to nearly two orders of magnitude. Average loadings of naturally fallen fuels were 49 Mg/ha two decades ago with some stands

accumulating up to 112 Mg/ha (Sackett 1979, Sackett et al. 1996). Annual

accumulations since then have been in the range of 1.3 to 7.8 Mg/ha/yr.

Tree densities that were once <130 stems/ha have increased dramatically, especially in dense thickets with more than 2,750 stems/ha (Sackett 1980,

Covington and Sackett 1986). Stand basal areas that were <11.5 m2

/ha prior

to removal of fire from ponderosa pine stands on the Mogollon Rim have

since increased by a three- or four-fold factor (Marlin Johnson, personal communication). Ponderosa pine stands reached a critical ecological point in 1991.

Fuel loads had so increased that by the end of the 20th century wildfires

1USDA Forest Service, Rocky Mountain

Research Station, Flagstaff, AZ.

2USDA Forest Service, Rocky Mountain

Research Station, Flagstaff, AZ.

3USDA Forest Service, Pacific Southwest

Research Station, Riverside, CA.
106 USDA Forest Service Proceedings RMRS-P-29. 2003.

consumed four times the area that they did in the period from 1910 to 1990

(Neary et al. 1999).

Carbon and Nitrogen in Ponderosa Pine Ecosystems

Fires can greatly alter nutrient cycles of forest ecosystems depending on fire

severity, fire frequency, vegetation, and climate (Neary et al. 1996). Responses

of total C and N are variable and depend on the site conditions and fire characteristics. In most soils, the majority of the N pool is contained in the soil

organic matter (OM). Mineral forms of N are usually lower but respond to

fire. For example, Grove et al. (1986) found no change in organic C in the

surface 0-1.2 in (0-3 cm) of soil immediately following burning; however,

percent total N increased. Knoepp and Swank (1993) found no consistent

response in total N in the upper soil layer, but increases in ammonium N

(NH4

-N) concentrations and N mineralization occurred on areas where a

burning treatment followed felling.

As would be expected, frequency of burning affects C accumulations. A

study was carried out on tropical savanna sites in Africa having both clay and

sandy soils that were burned repeatedly every 1, 3, or 5 years (Bird et al.

2000). While the clay sites had greater total C than did the sandy soils, they

responded similarly to burning. All unburned sites had 40-50 percent greater

C than burned sites. Low frequency burning (every 5 years) resulted in an

increase in soil C of about 10 percent compared to the mean of all burned

areas. High frequency burning (every year) decreased C about 10 percent. In

another study, Wells et al. (1979) reported the results of a 20-year burning

study in a pine plantation in South Carolina. They found that periodic burning over a 20-year period removed 27 percent of the forest floor. Annual

burning conducted in the summer removed 29 percent of the forest floor as

compared to a 54 percent loss resulting from winter burning. The total OM

content of the surface soil (0-5 cm) increased in all cases but there was no

effect on the 5-10 cm soil layer. Interestingly, when they summed the OM in

the forest floor and in the surface 0-10 cm of soil they found that these lowseverity periodic burns sites had not reduced, but only redistributed the OM.

Nitrogen in Ponderosa Pine Ecosystems

Prescribed fire has long been viewed as an important tool for restoring

ponderosa pine stands in the Southwest (Sackett 1980, Sackett et al. 1996).

The purpose of prescribed fire is to reduce fuel loads while promoting a healthy,

fire-resistant, and productive forest. Sackett (1980) established a set of studies

near Flagstaff, Arizona (Chimney Springs and Limestone Flats), to restore

overstocked ponderosa pine stands by introducing prescribed fire at 1-, 2-, 4-,

6-, 8-, and 10-year intervals. Since ponderosa pine growth is often limited by

low nitrogen (N) availability, a major concern with frequent prescribed fire is

the effect on soil N pools (Powers 1980).

Nitrogen is considered the most limiting nutrient in wildland ecosystems

and as such it requires special consideration when fire is managed, particularly

in N-deficient ecosystems (Maars et al. 1983). Nitrogen is unique because it is

the only soil nutrient that is not supplied to the soil by chemical weathering of

parent material. Almost all N found in the vegetation, water, and soil of wildland systems has to be added to the system from the atmosphere. The cycling

of N involves a series of interrelated complex chemical and biological processes.

Nitrogen pools can be severely disturbed by soil heating during the combustion process. Volatilization is the chemically driven process most responsible

Neary, Overby, and Haase Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona
USDA Forest Service Proceedings RMRS-P-29. 2003. 107

for N losses during fire. There is a gradual increase in N loss by volatilization

as temperature increases (Knight 1966, White et al. 1973). The amount of N

loss at different temperatures follows the heating sequence shown in table 1.

As a general rule the amount of total N that is volatilized during combustion

is directly proportional to the amount of OM destroyed (Raison et al. 1985a).

It has been estimated that almost 99 percent of the volatilized N is converted

to N2 gas (DeBell and Ralston 1970). At lower temperatures N2 can be produced during OM decomposition without the volatilization of N compounds

(Grier 1975). The N that is not completely volatilized either remains as part

of the unconsumed fuels or it is converted to highly available ammonium

nitrogen (NH4

-N) that remains in the soil (DeBano et al. 1979, Covington

and Sackett 1986, DeBano 1991).

Estimates of the total N losses during prescribed fire must be based on both

fire behavior and total fuel consumption because irregular burning patterns

are common. As a result, combustion is not complete at all locations on the

landscape (DeBano et al. 1998). For example, total N loss was studied during

a prescribed burn in southern California (DeBano and Conrad 1978). In this

study, only 10 percent of the total N contained in the plant, litter, and upper

soil layers was lost. The greatest loss of N occurred in aboveground fuels and

litter on the soil surface. In another study of N loss during a prescribed fire

over dry and moist soils, about two-thirds of the total N was lost during burns

over dry soils compared to only 25 percent when the litter and soil were moist

(DeBano et al. 1979). Although these losses were relatively small, it must be

remembered that even small losses can adversely affect the long-term productivity of N-deficient ecosystems.

Monleon et al. (1997) conducted understory burns on ponderosa pine sites

burned 4 months, 5 years, and 12 years previously. The surface soils, 0 to

5 cm, showed the only significant response. The 4-month burned sites had

increased total C and inorganic N following burning, and an increased C/N

ratio. Burning the 5-year-old sites resulted in a decrease in total soil C and N,

and a decrease in the C/N ratio. Total soil C and N in the surface soils did not

respond to burning on the 12-year-old site.

Nitrogen Losses — An Enigma

It has been conclusively established by numerous studies that total N is

decreased as a result of combustion (DeBano et al. 1998). The amount of N

lost is generally proportional to the amount of OM combusted during the

fire. The temperatures at which N is lost are discussed above. In contrast,

available N is usually increased as a result of fire, particularly NH4-N

(Christensen 1973, DeBano et al. 1979, Carballas et al. 1993). This increased

N availability enhances post-fire plant growth, and gives the impression that

more total N is present after fire. Increased fertility, however, is misleading

and short-lived. Temporary increase in available soil N following fire is usually

Table 1—Soil nitrogen loss with increasing temperature

(adapted from DeBano et al. 1998).

Stage Soil temperature (∞ C) Soil N loss (%)

1 <200 None

2 200-300 25-50

3 300-400 50-75

4 400-500 75-100

5 >500 100

Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona Neary, Overby, and Haase
108 USDA Forest Service Proceedings RMRS-P-29. 2003.

rapidly utilized by plants and microorganisms in the first few years after burning.

The consequences of N losses during fire on ecosystem productivity depend on the proportion of total N lost for a given ecosystem (DeBano et al.

1998). In N-limited ecosystems even small losses of N by volatilization can

impact long-term productivity. Consequently, a key ecosystem parameter that

has been studied in the ponderosa pine restoration study established by Sackett

(1980) is N.

Covington and Sackett (1986, 1992) examined N concentrations in the

upper 5 cm of mineral soil at the Chimney Springs burning interval study

(Sackett 1980). They found that mineral forms of N (NH4

-N and NO3-N)

made up <2% of the total N pool. Burning at 1- and 2-year intervals significantly increased only NH4

-N levels in the soil. Total soil N in the upper 5 cm

was not affected by prescribed fire interval. A later study (Wright and Hart

1997) assessed the effects of the two-year burning interval at the Chimney

Springs site. It inferred that repeated burning at two-year intervals may have

detrimental long-term effects on N cycling, along with depletion of the forest

floor and surface mineral soil C and N pools.

Methods

Study Sites

The original study sites established in 1976 and 1977 were designed to

determine the optimum-burning interval necessary to provide continuous fire

hazard reduction. These studies are described in greater detail by Sackett

(1980), Covington and Sackett (1986), and Sackett et al. (1996). Sites were

selected on volcanic soils at Chimney Springs, Fort Valley Experimental Forest, north of Flagstaff, Arizona, and sedimentary soils at Limestone Flats, Long

Valley Experimental Forest, near Clint’s Well, Arizona. Twenty-one 1.0 ha

plots make up each study site. There are three replications of unburned (control), and 1-, 2-, 4-, 6-, 8-, and 10-year prescribed fire treatments. All of the

burn rotation treatments, except for the 10-year rotation and controls, were

burned the previous October (2001).

Chimney Springs

The Chimney Springs study is located in the Fort Valley Experimental

Forest, Rocky Mountain Research Station, Coconino National Forest about

3 km northwest of Flagstaff, Arizona. Soils are Brolliar stony clay loam, a fine,

smectic, frigid Typic Argiboroll derived from basalt and cinders (Meurisse

1971). Stand structure and fuels are described by Sackett (1980). The original ponderosa pine stand was virtually undisturbed by wildfire since 1876 but

was grazed in the late 19th century and placed under fire control. At the initiation of the study, the ponderosa pine stand consisted of reproduction

(976 stems/ha), saplings (2,752 stems/ha), pole-sized trees (771 stems/ha),

and old growth (dbh >28 cm, 133 stems/ha). The basal area was 33.0 m2

/ha

in trees >10 cm dbh. The original fuel load of dead surface and ground fuels

was 34.0 Mg/ha.

Limestone Flats

The Limestone Flats study is located in the Long Valley Experimental

Forest, Rocky Mountain Research Station, Coconino National Forest, about

Neary, Overby, and Haase Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona
USDA Forest Service Proceedings RMRS-P-29. 2003. 109

2 km northwest of Clint’s Well, Arizona. Soils are very fine sandy loam

textured, fine, smectic, Typic Cryoboralfs. These soils developed from weathered sandstone with limestone inclusions. Stand structure and fuels are described

by Sackett (1980). The original ponderosa pine stand was treated with a sanitation cutting in the mid 1960s to remove trees attacked by insects and

disease. It was also grazed in the late 19th century, and placed under fire control, but grazing had been eliminated many years prior to 1976. The ponderosa

pine stand consisted of reproduction (1,373 stems/ha), saplings (2,881 stems/

ha), pole-sized trees (388 stems/ha), and old growth (dbh >28 cm, 82 stems/

ha). The basal area was 22.5 m2

/ha in trees >10 cm dbh. The original fuel

load of dead surface and ground fuels was 34.9 Mg/ha.

Soil Sampling

The soils at both the Chimney Springs and Limestone Flats sites were sampled

in late December 2002. The initial sampling location was randomly selected

within the center 400 m2 of each plot. The next two samples were located 5 m

from the first sample, selected by a randomization process, on two of the

cardinal directions from the first sample. The locations were not stratified by

stand structure or other site features as was done in the study by Covington

and Sackett (1986).

Approximately 500 g was collected from the 0-5 cm depth of the mineral

soil. The samples were air dried in the laboratory, sieved to a size of <2 mm,

and sub-sampled for analysis. Sub samples were ground to a 40 mesh particle

size then oven dried at 40o

 C.

Carbon and Nitrogen Analysis

Soil total C and N were analyzed on a Thermo-Quest Flash EA1112 C-N

analyzer. The computer-controlled instrument oxidizes samples at 1,500o C,

separates CO2 and NO2, by gas chromatography on a packed column, and

determines C and N content with a thermal conductivity detector. Analysis

was performed using a standard protocol for this instrument, which includes

blanks, certified soil standards, and quality control samples during operations.

Statistical Analysis

Data were analyzed using the SAS univariate ANOVA under the GLM Procedure (SAS 2000) and Tukey’s Studentized Range test for means separation

of C and N values (p = 0.05). The plot design is 6 treatments (burn intervals

1, 2, 4, 6, 8, 10 years) and a control (unburned) times 3 replicates of each

treatment and control.

Results and Discussion

Carbon

Total soil C levels in the Limestone Flats and Chimney Springs 0-5 cm

horizon exhibit two trends (figure 1). The first is that soil C is significantly

higher at Chimney Springs (table 2). The initial forest floor fuel loading (34.0

Mg/ha) was actually lower than the Limestone Flats loading (34.9 Mg/ha).

At the start of the study in 1976, the Chimney Springs site had a higher basal

area and nearly double the density of pole and old growth trees (Sackett 1980).

Covington and Sackett’s (1986) stratified sampling indicated higher levels of

Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona Neary, Overby, and Haase
110 USDA Forest Service Proceedings RMRS-P-29. 2003.

N (hence C) in old-growth stands. The random nature of the sampling in this

study may have picked up more of the sites at Chimney Springs that Covington

and Sackett (1986) identified as “sawtimber” (old growth). Soil classification

also explains the difference between the carbon in the Limestone Flats and

Chimney Springs soils. The latter were classified as Argiborolls belonging to

the Mollisol soil order, indicating that they have naturally higher organic matter contents than the Cryoboralfs (Alfisol soil order) found at Limestone Flats.

The second trend in the soil C data appears to be that burning at the 8-year

interval produced statistically significant higher soil C levels than the controls

and that burning in general increases total C in the mineral soil (table 3). The

C concentration in the soil increased from 2.856% in the control to 5.277% in

the 8-year burning interval. However, only the control and 8-year interval are

statistically different. These data reflect more of the variability in soil C detected in this random sampling approach than any burning interval trend. It is

evident that the prescribed fires reintroduced into the two sites have increased

soil C. Sackett et al. (1996) concluded that the best burning interval was 4

years for reducing fuel loads. That interval produced the intermediate C level

in the 0-5 cm depth of the mineral soil.

Nitrogen

Total soil N levels followed a similar trend as total soil C (figure 2). Total

soil N concentrations were mostly higher across the range of burning

0

1

2

3

4

5

6

7

0 1 2 4 6 8 10

FIRE INTERVAL - YEARS

LF Limestone/Sandstone Soils CS Basalt Soils

% CARBON

Figure 1—Effect of fire interval on soil total carbon (mean and standard error), Limestone

Flats and Chimney Springs burning interval study, Arizona.

Table 2—Studentized Tukey’s test for C and N by location, Limestone Flats and

Chimney Springs, Arizona, burning interval restoration studies.

Element Location Mean (%) Tukey’s test N

(p = 0.05)

Carbon Limestone Flats 3.543 A 21

Chimney Springs 4.478 B 21

Nitrogen Limestone Flats 0.221 A 21

Chimney Springs 0.287 B 21

Neary, Overby, and Haase Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona
USDA Forest Service Proceedings RMRS-P-29. 2003. 111

intervals. Concentrations increased from an average of 0.188% in the control

plots to 0.330% in the 8-year burning interval (table 3). Soil N at Chimney

Springs with Typic Argiboroll soils was significantly different from Limestone

Flats with Cryoboralf soils (table 2). Significant differences in total N concentrations where found between control and 2-year burning interval and the 8-year

burning interval plot (table 3).

Covington and Sackett (1986) reported that <2% of the soil N measured in

their mid 1980s sampling was mineralized N (NH4

-N and NO3-N). The data

from this sampling conflict with Wright and Hart’s (1997) hypothesis that

burning at 2-year intervals may have detrimental long-term effects on N cycling, along with depletion of the forest floor and surface mineral soil C and N

pools. The 2-year burning interval was not significantly different from the

control or other burning intervals, only the 8-year burning interval. Wright

and Hart (1997) did not investigate the 1-year burning interval, yet our sampling showed it to be at an intermediate level of N in the 0-5 cm horizon. The

Table 3—Studentized Tukey’s test for C and N by treatment, Limestone Flats and

Chimney Springss, Arizona, burning interval restoration studies.

Burning interval Tukey’s test

Element (years) Mean (%) (p = 0.05) N

Carbon 0 2.856 A 6

2 3.210 AB 6

6 3.942 AB 6

1 4.024 AB 6

4 4.294 AB 6

10 4.476 AB 6

8 5.277 B 6

Nitrogen 0 0.188 A 6

2 0.212 A 6

6 0.228 AB 6

1 0.242 AB 6

10 0.281 AB 6

4 0.298 AB 6

8 0.330 B 6

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 1 2 4 6 8 10

FIRE INTERVAL - YEARS

LF Limestone/Sandstone Soils CS Basalt Soils

% NITROGEN

Figure 2—Effect of fire interval on soil total nitrogen (mean and standard error), Limestone

Flats (LF) and Chimney Springs (CS) burning interval study, Arizona.

Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona Neary, Overby, and Haase
112 USDA Forest Service Proceedings RMRS-P-29. 2003.

soil N pool does not provide a readily available source of N to plants and

microorganisms because of the slow decomposition rates in these semi-arid

ecosystems. This limitation, rather than any declines in the total soil N pool,

may account for the N enigma that DeBano et al. (1998) discuss.

Sample Variability

The lack of a strong burning interval response in this study was most likely

affected by site variability and the random sampling used. To obtain an understanding of the variability in soil total C and N, individual plot data is quite

instructive (figure 4).

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1234567

FIRE INTERVAL - YEARS

LF Limestone/Sandstone Soils CS Basalt Soils

% TOTAL NITROGEN

Figure 3—Total nitrogen (mean and standard error) in the A horizon for the Limestone Flats

(LF) and Chimney Springs (CS) burning interval study, Arizona.

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Fire Interval - years

Limestone Flats % Soil C Chimney Spring % Soil C

Unburned 1 2 4 6 8 10

% C

Figure 4—Variability in percent C in mineral soil, Limestone Flats and Chimney Springss

burning interval study, Arizona.

Neary, Overby, and Haase Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona
USDA Forest Service Proceedings RMRS-P-29. 2003. 113

The 1-year burning interval plot samples for total C at Limestone Flats

ranged from 2.933% to 3.796%, a span of 0.863%. The unburned control

samples had a range from 2.630% to 3.160%, a similar span of 0.530%. The

10-year burning interval plots at Limestone Flats had the highest variability.

Total soil C in the 0 to 5 cm depth ranged from 1.717% to 5.709%, a span of

3.991%. The unburned control plot samples at Chimney Springs had a range

from 2.367% to 3.711%, a span (1.344%) nearly triple that of the Limestone

Flats control. Within plot variability was much higher at Chimney Springs

than at Limestone Flats (figure 4).

The total C and N variability observed from the random samples at the

Chimney Springs and Limestone Flats sites was probably influenced by a number of factors. Covington and Sackett (1986) stratified their sampling at

Chimney Springs by stand type (e.g., sawtimber, poles, and saplings). It was

very evident during the sampling that there were visually evident differences

in the levels of litter accumulations and OM concentrations in the mineral soil

under these three different stand types. In addition, several other factors appeared to be important. Samples collected in the middle of clearings and next

to decaying, but not completely burned, logs had visually apparent differences

in color that reflected OM content. Another factor that could be important,

but was not readily discernable on the ground, is the presence of “hot spots”

where dead and decaying logs were at some point in time completely combusted by the prescribed fires. These logs would create zones of high fire

severity that would burn much of the soil OM and drive off most of the surface mineral soil N (DeBano et al. 1998).

Our recommendation as a follow-up to this study is to resample using

Covington and Sackett’s (1986) stand classification approach (i.e., sawtimber,

poles, and saplings), but add in areas such as clearings, decaying logs, and

high-severity burn spots. Using a composite sample of several cores would

also aid in the leveling of variability of the samples. While the classification

does allow easy scaling up to stand and landscape levels, the other categories

do not. That is why random sampling is still of interest. Some work is still

needed to determine sample sizes needed to detect differences between the

individual burning intervals, if such differences exist at all.

Summary and Conclusions

The effects of burning intervals for restoration of ponderosa pine stands on

total C and N concentrations in the 0-5 cm horizon of two different soil types

was examined. The burning intervals (unburned, 1, 2, 4, 6, 8, and 10 years)

were provided by a study established in 1976 and 1977 and have been maintained thereafter (Sackett 1980, Sackett et al. 1996). Although there were

statistically significant differences between the total C levels in soils of the

unburned plots and the 8-year burning interval, there were no differences

between burning intervals. There also was a statistically significant difference

between unburned and 2-year burning interval and the 8-year burning interval in total soil N. This study determined that burning increased mineral soil

C and N, which conflicted with Wright and Hart’s (1997) contention that the

2-year burning interval could deplete soil N and C pools. This study did not

examine the mineral fractions of the soil N pool, NH4

-N, and NO3-N. Although the mineral forms of N are small (<2 % of the total soil N pool), they

are very important for plant nutrition and microorganism population functions. It is recommended that the study be repeated contrasting stratified

sampling and higher intensity random sampling approaches.

Effects of Fire Interval Restoration on Carbon and Nitrogen in Sedimentary- and Volcanic-Derived Soils of the Mogollon Rim, Arizona Neary, Overby, and Haase
114 USDA Forest Service Proceedings RMRS-P-29. 2003.

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116 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 117

Mt. Trumbull Ponderosa Pine

Ecosystem Restoration Project

Ken Moore1, Bob Davis2, and Timothy Duck3

Abstract–This paper describes recent and current studies on the Parashant National

Monument in northern Arizona. These studies support the Mt. Trumbull Ponderosa

Pine Ecological Restoration Project. A major goal of the project is to allow wildfire to

resume its historic role in maintaining forest health following decades of declining

sustainable conditions.

Background

The frequent, low intensity fire regime that naturally occurred in southwestern ponderosa pine forests (Covington et al. 1999) was disrupted

when European-American settlers arrived in the area in the late 1800s and

early 1900s. Livestock grazing removed fine fuels that carry fire and logging

altered the age class structure of forests. Wildfire suppression reduced the

number of acres burned and increased the fire return interval. As a result,

much of the ponderosa pine forest in the southwest is functioning outside of

the range of natural variability and is in poor and declining health.

The most famous use of timber from the area was for use in the Church of

Jesus Christ of Latter Day Saints’ St. George Temple. Due to the technological limitations of logging using mule and oxen to skid in the remote, rugged

mountains, as well as those imposed by the use of portable steam sawmills

(which were not able to handle the largest trees), a sizeable portion of the old

growth was not harvested and remains standing today.

Today the forest consists of the remnants of the pre-settlement forest along

with approximately 100 years of regeneration. The previously open, park-like

stand, with tree densities around 50 per acre (Mast et al. 1999), has changed

to “dog hair,” with stand densities over 400 trees per acre. These stands are

susceptible to disease, insect infestations, catastrophic wildfire, and drought

(figure 1).

Introduction

For the past seven years, the Bureau of Land Management, working with

the Ecological Restoration Institute (ERI) at Northern Arizona University

and the Arizona Game and Fish Department, have been implementing a large

scale, ponderosa pine ecosystem restoration project in the Mt. Trumbull/Mt.

Logan area.

The Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project involves

public lands managed by the Bureau of Land Management within the newly

designated Grand Canyon-Parashant National Monument in northern

1Natural Resource Specialist, Parashant

National Monument (PNM), St. George,

UT.

2Forester, PNM, St. George, UT.

3Ecologist, PNM, St. George, UT.
118 USDA Forest Service Proceedings RMRS-P-29. 2003.

Arizona. As a “sky island,” the approximately 17,000 acre ponderosa pine

forest sits atop 7,000-8,000 ft mountains amid a piñon-juniper/sagebrush/

grassland community. The forest contains 500-year-old ponderosa pine along

with old growth piñon, juniper, and New Mexican locust.

The goals of the Mt. Trumbull restoration project are to 1) restore presettlement ecosystem health and function to a ponderosa pine forest in the

recently designated Parashant National Monument, just north of the Grand

Canyon; 2) reduce fuel loads and disrupt fuel continuity to reduce the risk of

catastrophic wildfire; and 3) gather information on a wide variety of ecosystem components and processes in order to understand the effects of restoration treatments. By reducing fuel loads and reestablishing an herbaceous

understory we hope to one day restore natural fire processes. Treatments

include merchantable and non-merchantable timber harvesting, prescribed

fire, and seeding of native herbaceous species. Due to the existing high fuel

loads, a variety of techniques have been used to reduce mortality of pre-settlement

ponderosa pine trees from treatment activities.

The project includes an adaptive management provision that allows managers to incorporate lessons learned into future treatments. For example, current treatments leave more trees than the earliest efforts. These additional

“insurance trees” allow for some mortality from natural and anthropogenic

impacts (such as prescribed burning).

Figure 1—“Dog hair” stand of post-settlement ponderosa pines at Mt. Trumbull, Arizona.

Moore, Davis, and Duck Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project
USDA Forest Service Proceedings RMRS-P-29. 2003. 119

The project can be broken out into seven distinct phases or efforts:

1. Project planning and regulatory compliance

2. Ecosystem inventory and timber marking

3. Merchantable and non-merchantable thinning

4. Prescribed burning

5. Restoration of native grasses and forbs by seeding

6. Maintenance of periodic low intensity grass fires in restored areas

7. Monitoring (occurs throughout all phases of the project)

Project Planning and Regulatory Compliance

This project was initiated prior to the designation of the area as a National

Monument. Current efforts must balance the needs of the restoration experiment with Monument values. In order to ensure that project impacts do not

impair Monument values and to provide our public interests with sufficient

information to understand the project and its anticipated effects, we prepare

environmental assessments for all treatment activities.

Ecosystem Inventory and Timber Marking

Table 1 shows pretreatment forest stand density data compiled from 128

20 ¥ 50 meter ecosystem monitoring plots established by Northern Arizona

University within all existing and proposed ecosystem restoration units. In

these plots researchers tallied all stems of ponderosa pine, gambel oak (Quercus

gambelii ), juniper (Juniperus osteosperma), and piñon pine (Pinus edulis ) ≥1

inch dbh. Stems/plot were extrapolated to stems/acre for the entire restoration

Table 1—Pretreatment forest stand density.

Overstory Pole Overstory Pole Overstory: Pole: Overstory Pole

Unit(# plots) ponderosa ponderosa oak oak juniper/piñon juniper/piñon locust locust Total

Lava (3) 84 108 46 17 - / 4 - / - - 11 270

Trick Tank (11) 131 83 82 111 - / - - / 6 1 15 429

Rye Flat (10) 126 100 42 67 7 / 2 7 / 5 - - 356

N. Sawmill (8) 229 263 7 44 29 / 2 67 / 27 - - 668

High Meadow (16) 117 103 74 166 6 / 2 9 / 13 3 6 499

Lower Sawmill (5) 57 26 62 76 26 / 9 29 / 16 - 10 311

EB-1 (4) 55 33 3 5 - / - - / - - - 96

EB-2 (4) 88 57 117 50 - / - - / - - - 312

EB-3 (5) 62 48 - - - / 1 - / - - - 111

EB-4 (2) 260 577 - 41 10 / - 33 / 33 - - 954

EB-5 (6) 168 89 22 22 29 / 2 54 / - - - 386

Cinder (8) 87 27 33 22 - / - - / 3 1 - 173

Hi-Lo (13) 134 103 20 73 14 / 7 20 / 15 20 10 416

Nixon (9) 124 100 20 4 18 / 4 38 / 13 - - 321

Spring (10) 64 26 15 34 30 / 18 23 / 55 - - 265

Boundary (4) 55 11 16 11 19 / 18 49 / 43 - - 222

War Club (4) 104 106 1 16 22 / 3 174 / 24 - - 450

Corner (6) 154 252 2 16 25 / 10 68 / 25 - - 552

Mean stems/acre 117 117 31 43 13 / 4 31 / 15 1 3 377

Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project Moore, Davis, and Duck
120 USDA Forest Service Proceedings RMRS-P-29. 2003.

units. The table shows overstory stems (≥5.9 in. dbh) and pole stems (≥1 in.,

£ 5.9 in. dbh) and total stems per acre for the restoration units.

Ecosystem monitoring plots used to collect the data shown in table 1 are

based on National Park Service long term vegetation and fuels monitoring

plots as described in the Western Region Fire Monitoring Handbook (1992).

Under conventions described in this handbook, all trees greater than or equal

to 5.9 inches dbh are classed as overstory trees. Trees that are greater than 1

inch and less than 5.9 inches are classed as pole trees. Growth increment core

samples, used to determine age, are taken from all live pre-settlement trees on

each long term ecosystem monitoring plot. In addition, a 10% random sample

of all living post-settlement age trees are cored for accurate age determination. This information is available upon request from the BLM, St. George

Field Office.

The ecosystem monitoring plots established by NAU in proposed treatment areas and in the control area show that current forest conditions are

dense. Overstory, pole, and regeneration data show a mean of 569 trees/acre

in the treatment unit plots and 600 trees/acre in the control unit plots. Treatment unit plots and control unit plots show very similar composition before

implementation of restoration prescriptions on treatment areas, with 47% and

48% ponderosa pine, respectively and 30% and 33% gambel oak, respectively.

Oak is a dominant species in terms of tree density, but makes up only 10.5%

and 13% of the basal area in treatment and control unit plots respectively.

Juniper and piñon pine have higher densities in treatment unit plots. New

Mexico locust appears to have higher densities in the control unit plots but

variability is very high due to the patchy distribution of locust. In terms of

basal area, juniper, piñon, and locust collectively contribute less than 10% of

the stand basal area on treatment unit plots, and less than 3% on control unit

plots (NAU 1997).

Diameter distributions show that 50% of current live trees are less than 20

cm (7.8 in.) dbh. Of these smaller trees, ponderosa pine are the major component. Numbers of non-ponderosa pine drop off at the higher diameter classes

(NAU 1997). NAU has reconstructed the forest structure from long term

ecosystem monitoring plots. 1870 was chosen as the pre-disturbance date

based on preliminary fire scar data, which shows the last widespread naturally

occurring fire to be in 1870, in addition to logging and grazing records, which

place domestic livestock grazing in the Mt. Trumbull area around 1870

(Altschul and Fairley 1989). Increment cores were taken from all live trees

identified as pre-settlement in the field, based on conservative size criteria and

bark coloration. After laboratory analysis of the tree core data, age data were used

to confirm or reject the field determination. For each cored pre-settlement tree of

any species, the 1870-1996 radial growth increment was measured on the

core and the 1870 diameter at breast height was calculated. For dead trees

(snags and logs), the year of death of each tree was estimated based on tree

diameter and condition class. To determine the 1870 diameter of dead trees,

growth estimates over the 1870 to death date periods were applied. For these

preliminary data, NAU used a predictive regression relationship between diameter and basal area increment that was developed for pre-settlement pine

trees from samples taken from Camp Navajo Army Depot, Bellemont, AZ.

Results from plots show the reconstructed 1870 forest had an average of 22

trees/acre (55 trees/ha) versus 349 trees/acre (863 trees/ha) in 1996 for all

species, while basal area in the reconstructed forest was only 35.7 ft2

/acre

versus 130.7 ft2

/acre in 1996. These values are within the low range of presettlement forest densities and basal areas found in ponderosa pine forests in

the southwest. Pine density in the Mt. Trumbull area falls within the range of

Moore, Davis, and Duck Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project
USDA Forest Service Proceedings RMRS-P-29. 2003. 121

7-116 ponderosa pine/ha reported in several early National Forest inventories and other studies in the region (Covington and Moore 1994).

Merchantable Timber Harvest and

Non-Merchantable Thinning

BLM uses a combination of timber sales, thinning contracts, and its own

workforce to reduce the density of trees within restoration units. Only postsettlement trees are cut. Timber sales remove between 2,500 and 3,000 board

ft/ac from the units. The primary products are boards, poles, palettes, grape

trellis, sawdust, and fuelwood. Trees less than 18" DBH are logged at a loss,

which is amortized by profits from larger trees (up to 28"). Loggers are required to protect residual trees from their harvest operations. Loggers are

hauling 12"-28" trees, all post-settlement.

Early in the project BLM reduced the maximum size of ponderosa that was

allowed to be cut from 28" to 22" and received no bids; the project lost one

year due to the failure to attract any commercial partners. We now include

larger (up to 28" DBH) post-settlement trees, although few of the trees cut

are of the maximum size. We now mark more leave trees than we did originally to provide insurance against losses during burning and logging operations.

In addition, ladder fuels composed of post-settlement ponderosa pine, piñon

pine, or juniper trees of less than 10 inches diameter breast height (dbh) are

thinned with power saws. Post-settlement trees are limbed with power saws to

reduce fire spread from the forest floor into the canopy. Pre-settlement trees

are limbed to a height of approximately 6-8 feet. Snags greater than 12 inches

dbh are prepped (e.g., fireline is constructed around these features) in an

effort to reduce ignition.

The BLM fuels reduction crew prepares units for burning by reducing the

height of any activity fuels, thinning unmerchantable trees, preparing firelines,

and raking litter from the base of pre-settlement trees. Logging slash is lopped

and scattered, then a low intensity broadcast burn is started under environmental conditions conducive for burning (hot and dry enough to allow for

consumption of litter, but not so hot and dry as to allow tree mortality from

scorch height or canopy damage).

Consumption of deep layers of forest floor litter generates high amounts of

heat energy. Studies at Fort Valley and Long Valley Experimental Forests

showed very high mineral soil temperatures during burning. Smoldering

combustion takes place in deep layers of forest litter and may continue for

durations of 72 hours. Burning for this length of time can result in either

temperatures exceeding 140∞F, causing cambium death, or lower temperatures for longer durations, which also cause tissue death (Harrington and Sackett

1988).

Prescribed Burning

Following preparation of burn units, BLM introduces low intensity prescribed fires to reduce duff depth from a range of 1-10" to a range of .25-2",

and to reduce fuel loading from an average of 10 tons per acre to a range of

1-6 tons per acre. To prevent degradation of the Grand Canyon airshed,

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122 USDA Forest Service Proceedings RMRS-P-29. 2003.

favorable winds that carry particulate matter away from the canyon are necessary. Sagebrush that exists in places as a significant understory shrub are not

excluded from prescribed fire.

Table 2—Prescription parameters in the use of prescribed fire.

Prescription parameters Prescribed fire behavior

(forest understory) Cool Hot Desired Outside (max.)

Fuel model (s) 9999

Live fuel moisture (%) n/a n/a n/a n/a

1000 hr. moisture (%) 16% = 50% 6% =90% 10% =75% 6%

used in lieu of duff reduction duff reduction duff reduction

duff moisture

Soil moisture (%) n/a n/a n/a n/a

Rate-of-spread (ch/hr) 2 chains/hour 2-6 chains/hour 2 chains/hour 2-6 chains/hour

Flame length (ft) 1 foot 2-3 feet 2 foot 2-3 foot

Scorch height (ft) 2 feet 3-4 feet 1 foot 3-4 feet

Type of fire strip headfire backing backing

Probability of ignition (%) 60%

Spotting distance (mi) .3 - .4 mile

Table 3—Environmental parameters in the use of prescribed fire.

Environmental parameters Acceptable range

(forest understory) Cool Hot Desired

Air temperature (degrees F) 30 ̊ 75 ̊ 50 ̊

Relative humidity (%) 46% 20% 35%

Wind speed (midflame, mph) 2 8 5 Outside burn block

Wind direction west northeast, south, at critical holding

northwest, southwest, point—minimum

east southeast acceptable moisture

1 hr fuel moisture (%) 11 5 7 5

10 hr fuel moisture (%) n/a n/a n/a n/a

1000 hr fuel moisture (%) 16 6 10 6

Restoration of Native Grasses and Forbs

by Seeding

The herbaceous and shrub understory of southwestern ponderosa pine forests is important for many ecological reasons, including food and habitat for

herbivores, nutrient cycling, soil formation and stabilization, and its contribution to biological diversity and aesthetic appeal. Understory species composition and cover have been adversely modified since Euro-American settlement

and understory production has declined with increasing tree density. Key to

restoring the understory is an understanding of the on-site plant material,

including above ground vegetation as well as viable seeds in the soil bank.

To obtain a baseline of the species in the current soil seed bank at Mt.

Trumbull, soil samples were collected in September 1997 from 5 vegetation

types within three of the experimental block units. Soil samples were collected

in April 1997 from unburned, lightly burned, and severely burned patches

within the 33 acre subunit of the 96-1 restoration unit. Samples were placed

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USDA Forest Service Proceedings RMRS-P-29. 2003. 123

in the NAU greenhouse for several months. Germination from these samples

was compared to samples taken in a control (untreated) area to determine

where recruitment of plants from the seed bank occurs in a restored area.

Preliminary results indicate that a large number of seeds survive in the soil

seed bank in unburned and lightly burned patches within the landscape mosaic. The survival of these seeds has implications for seeding and recruitment,

as well as exotic species control. Samples will be collected in the future from

the experimental units to document changes in the seed bank over time and

with ecological restoration treatments.

Further research is needed to determine if perennial grasses and forbs utilize the soil seed bank for seed storage. The majority of species that store seeds

in the soil seed bank are early successional annuals. In order to restore areas to

historic species composition and frequency, seeding of species that have been

extirpated from an area may be required, especially if those species are not

found in the soil seed bank. BLM has been applying seed to the units after

they have been burned in order to reduce exotic weed invasion and to assess

the effectiveness of seeding.

Efforts to use local genotypes are made, although local varieties are not

always available. Some seed used on the project was collected far way, even

though the species is considered to be an historic native of Mt. Trumbull.

Seed is applied using hand-held spreaders, usually within six months of the

burn treatment. Open sites are “back-dragged” using rakes or chains dragged

behind an ATV.

Table 4—2001 Rye Flat seeding. Example of seed mix and application rates

used at the Mt. Trumbull Restoration Project.

Total weight Lbs/acre

Species (lbs PLS*) (80 acres)

Big bluegrass (Poa ampla) 50 0.6

Sand dropseed (Sporobolus cryptadrus) 40 0.5

Sideoats grama (Bouteloua curtipendula) 120 1.5

Utah serviceberry (Amelanchier utahensis) 40 0.5

Fringed sagebrush (Artemisia frigida) 40 0.5

Wood’s rose (Rosa woodsii) 40 0.5

Arizona fescue (Festuca arizonica) 140 1.75

Prairie Junegrass (Koeleria cristata) 80 1.0

Bottlebrush squirreltail (Sitanion hystrix) 100 1.25

Slender wheatgrass (Agropyron trachycaulum) 100 1.25

Mountain brome (Bromus marginatus) 100 1.25

Western wheatgrass (Agropyron smithii) 100 1.25

TOTAL 950 11.85

* Percent live seed

Maintenance of Periodic Low Intensity Grass

Fires in Restored Areas

Due to delays in implementing the project, re-entries have not yet occurred.

The plan is to complete the restoration process phase 1 throughout the area,

then re-enter blocks with fire as funding and conditions allow.

Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project Moore, Davis, and Duck
124 USDA Forest Service Proceedings RMRS-P-29. 2003.

Monitoring

The Mt. Trumbull Ecosystem Restoration Project combines landscape scale

operational restoration treatments with comprehensive research. The Ecological

Restoration Institute at NAU and the Arizona Game and Fish Department

are heading up the research efforts tied to BLM’s operational treatments. The

research/monitoring studies accompanying this project are varied, but focus

on the effects of ponderosa pine ecosystem restoration treatments on vegetation structure and composition, and wildlife. To determine the effects of treatments upon vegetation, 250 long term ecosystem monitoring plots have been

established across 4,500 acres of ponderosa pine forest. In addition, studies

are in place to monitor the effects of restoration treatments upon small mammals, Abert’s squirrels, mule deer, bats, Merriams’s turkey, reptiles, passerine

birds, and insects.

To achieve sustainable management of ponderosa pine forest ecosystems, a

scientific basis must be developed for understanding the structure and dynamics of healthy ecosystems. To quantify the effects of present day disturbances,

forests where high-intensity wildfires, bark beetle outbreaks, and other factors

are resetting the pattern of succession must be measured and compared.

However, NAU and BLM believe the threat to current unsustainable ecosystems is so imminent that carefully planned and closely monitored treatments

to restore ecosystem health, based upon the best existing knowledge and designed to adapt rapidly as new information is learned need to be carefully

initiated now. NAU, AGFD and BLM are providing a program of scientific

study which is providing the information and technologies needed by BLM to

carry out ecosystem restoration in an adaptive, “learn by doing manner.” NAU,

AGFD and BLM are providing an integrated plan of research, experimentation, and feedback, intended to lay the groundwork for integrated ecosystem

management by providing scientists, land managers, and the public with tested

procedures for restoring ecosystem health.

NAU Scientific Study

NAU scientific study consists of three elements:

- systematic analysis of changes in forest structures and disturbance regimes

since disruption of the natural fire regime;

- comparison of the effects of contemporary disturbances from high-intensity wildfire, bark beetle mortality, prescribed fire, and understory thinning; and

- initiation of restoration treatments in an adaptive management setting.

Sampling is occurring at a series of study areas, ranging from those with

extended fire exclusion to those still under frequent regimes. Sampling is being carried out at Mt. Trumbull, Beaver Creek Biosphere Reserve, Arizona;

the Sierra Tarahumara, Chihuahua, Mexico; La Michilia Biosphere Reserve,

Durango, Mexico; and other sites within the southwestern ponderosa pine

forest. NAU has carefully selected sampling sites that maximize efficient collection of data most valuable to ecological understanding and management

planning at regional, national, and international levels. By applying the same

sampling and analysis methods on a range of study areas stretching across the

southwestern ponderosa pine ecosystem of North America, this systematic

approach is aiding in producing a unified, comparable data set for addressing

the natural range of variability of pre-disruption forests, as well as the range of

contemporary forest conditions. These quantitative data are being used to support the development of operational ecosystem restoration and management

Moore, Davis, and Duck Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project
USDA Forest Service Proceedings RMRS-P-29. 2003. 125

plans across portions of the Southwest, not only within the Mt. Trumbull/

Logan area.

Specific NAU studies include:

1) Permanent ecosystem monitoring plots for long-term monitoring.

Long-term monitoring is essential to assess current conditions, reconstruct

pre-settlement ecosystem structure, and evaluate the effects of restoration treatments. Monitoring involves recordation of pre-treatment data, assessing current conditions, followed by multiple post-treatment recordation of data and

analysis on the same plots to determine changes to the ecosystem. Permanent

monitoring plots have been established on a 300 ¥ 300 meter grid across Mt.

Trumbull. A total of 249 permanent ecosystem monitoring plots are in place

across approximately 4,800 acres of ponderosa pine/gambel oak forest.

2) Experimental blocks.

The experimental blocks are designed for detailed measurements of the

effects of ecosystem restoration treatments, and complement the landscape

scale sampling provided by the permanent ecosystem monitoring plots for

long-term monitoring described above. The purpose of the experimental blocks

is to quantify restoration effects through intensive measurements at a series of

five controlled study sites. Each block consists of a 50-140 acre area divided

into two similar units. Treatments—ecosystem restoration and control—have

been randomly assigned to each unit. Basic measurements at each experimental block include contemporary and pre-settlement (pre-1870) forest structure (species composition, size distribution, age distribution), current

understory composition and density, canopy closure, fuel loading, and photographs.

3) Passerine bird habitat and population responses to ecological restoration.

The diversity and abundance of passerine birds is being quantified with

point counts over approximately 2000 acres in the Mt. Trumbull/Logan area

using a before-after-control-impact-pairs design. Habitat characteristics, assessed on the permanent monitoring plot grid, are correlated with bird counts.

Baseline data collected over three breeding seasons (1996-98) will be compared with post-treatment data.

4) Restoration of grasses, wildflowers, and shrubs.

The objective of this study is to develop practical methods for restoring

native understory diversity and productivity. Key to restoring the understory

is an understanding of the on-site plant material, including above ground vegetation as well as viable seeds in the soil bank. Soil samples were collected

from different treatment areas and germination compared to samples taken in

a control (untreated) area.

5) Butterfly response to ecological restoration

Butterflies are an excellent group to study to examine changes in habitat.

Because the butterfly larval stage is typically host specific, butterfly diversity

can be a good indicator of host-plant species diversity. Different species of

butterflies feed on a variety of plants, including trees, shrubs, grasses, and

forbs. Because both adult and larval stages are important food sources for

birds and other animals, population studies of this trophic level will provide

habitat information complementing studies on birds and mammals. Butterfly

species diversity and abundance is examined before and after treatment in the

restoration units, as well as compared between control and treated units. Primarily, NAU is looking for changes in butterfly community structure in response to the changes in forest structure and herbaceous structure caused by

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126 USDA Forest Service Proceedings RMRS-P-29. 2003.

the restoration treatments. In addition, analysis of abundance and species data

is occurring, as well as sampling of butterfly communities in pre-settlement

like forests.

6) Effects of high-intensity fire.

This study focuses on measuring ecosystem structure (trees, shrubs, grasses,

forbs, dead biomass) in sites burned with high-intensity fire, reconstructing

pre-burn conditions where possible, and comparing present and forecast conditions over the burned area. Two severe wildfires in the Mt. Trumbull/Logan

area were sampled in 1996, the Lava fire (approximately 20 acres), and the

Logan fire (approximately 150 acres). This study follows permanent plots

over several years in a forest which burned with high intensity following approximately 60 years of fire exclusion.

7) Effect of thinning and sprouting in gambel oak.

The purpose of this research is to examine the potential ecological restoration of gambel oak, specifically by discovering ways in which the growth rate

of existing old-growth gambel oak can be increased and how the growth rate

of replacement large-sized oak can be enhanced. Two interrelated questions

regarding the ecological restoration of gambel oak addressed in this research

are: 1) Does thinning within oak clumps result in an increase in the growth of

residual large oaks? 2) What is the effect of sprouting on the growth rate of

large oak trees?

8) Wilderness restoration study.

Thirty-four ecosystem monitoring plots in the Mt. Logan Wilderness Area

were analyzed in 1998, and the data was used to reconstruct the forest structure in 1870, the date of the last natural fire in the wilderness area. In addition, a social survey is being conducted to assess public response to wilderness

restoration. A social survey to assess the local populations of St. George,

Hurricane, Fredonia, Colorado City, and Kanab on their attitudes towards

Mt. Logan Wilderness restoration treatments has been completed.

9) A potential wilderness treatment: forest restoration without wood removal.

This study will apply a variation of a full restoration treatment that does not

remove any wood from the sites. All logs and slash generated from the thinning have been left on the ground. Cut trees were limbed to put as much

wood as possible in contact with the ground to accelerate decomposition. The

logs were bucked into four-foot sections to reduce the chances of an Ips beetle

outbreak and slash was moved from under the crowns of the residual trees to

prevent fire damage. Comparisons will be made between this technique and

conventional wood removal techniques to an untreated area. The effects to be

studied include site damage, understory recovery, and fire behavior.

10) Dendroclimatic reconstruction.

Increment cores collected in 1996 from old ponderosa pine trees were cross

dated and measured to develop a long tree-ring width chronology for the Mt.

Trumbull area. Currently, a third dendrochronologist is checking the chronologies to improve correlations. This chronology will be correlated with

weather data to create a model that will reconstruct past climatic trends and

will be compared with tree age data and fire history results to search for past

climate-plant-disturbance connections and to suggest potential future changes.

11) Fire History.

Frequent, low intensity fire regimes are characteristic of ponderosa pine

throughout its range, but specific knowledge of the characteristics of

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USDA Forest Service Proceedings RMRS-P-29. 2003. 127

pre-settlement fire patterns in the Mt. Trumbull/Logan area is important to

guide the reintroduction of fire and to permit evaluation of the restored fire

disturbance regime. Fire history reconstruction based on dendrochronological measurement of fire scarred trees, stumps, and logs is being undertaken in

the Mt. Trumbull/Logan area to estimate the frequency and seasonality of

pre-settlement fire, determine the date of fire exclusion, and develop a record

of post-settlement fires. A key feature of the project is the landscape scale of

sampling across the entire ponderosa pine forest, allowing researchers to

explore questions of fire size, intensity, and variability within the ecosystem,

issues that have exceeded the scope of many previous studies.

12) Modeling forest structure change.

The success of management is largely based upon the ability to predict the

consequences of alternative actions. Assessing the future cumulative impacts

of ecosystem management practices, involving highly complex and variable

interactions, is challenging. NAU is applying a process simulation model,

FIRESUM, to forecast the effects of alternative forest structural conditions,

understory and fuel characteristics, and fire occurrence. FIRESUM was designed to integrate fire behavior and effects modeling together with ecological process modeling. The model has been calibrated to southwestern ponderosa

pine forests and will be initialized with data from the permanent monitoring

plots in the Mt. Trumbull/Logan area. Comparisons among past and present

ecosystem structures, as well as potential future alternatives, will be made.

13) Habitat relationships of the Kaibab squirrel and other sciurids.

In 1997 a project was initiated to investigate habitat relationships of Kaibab

squirrels, rock squirrels, and cliff chipmunks in the five Mt. Trumbull ecological restoration experimental blocks (EB-1 through EB-5). As a continuation

of that study, track stations and a tassel-eared squirrel feeding index were used

in the five experimental blocks in May and August of 1998 in these same

locations which were used in August of 1997. This study will provide information on the nature and distribution of Kaibab squirrel feed trees and their

relation to restoration treatments, as well as habitat use information.

14) Indigenous land management practices.

In 1998, a project was initiated to explore indigenous land management

practices, focusing on the Kaibab Paiute band. In particular, researchers are

seeking to determine the use of fire in the Paiute culture, as well as Paiute

knowledge of the ethnobotanical uses of the plants found in the Mt. Trumbull

areas. This information will be used to explore relationships between indigenous knowledge and contemporary restoration practices.

Arizona Game and Fish Department Scientific Study

NAU studies focus mainly upon vegetative response to restoration treatments. Given the acknowledged need to incorporate a wildlife component into

this research, the Arizona Game and Fish Department (AGFD) has taken the lead

in securing additional funding and devising studies to determine the effects of

restoration treatments upon a variety of wildlife species. AGFD has assumed

responsibility for overall wildlife research coordination and implementation.

AGFD has identified a number of focal wildlife species and species groups

for evaluating the effects of forest restoration treatments. Focal species were

selected and evaluated with respect to the following criteria:

- their use of or association with habitat components that will be altered by

restoration treatments;

Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project Moore, Davis, and Duck
128 USDA Forest Service Proceedings RMRS-P-29. 2003.

- AGFD’s ability to measure changes in population density and/or habitat

use;

- roles in ecosystem function; and

- economic/recreational value.

The selected species and species groups are: passerine birds, mule deer,

Kaibab squirrel, Merriam’s turkey, and herpetofauna. Sampling arrays for these

focal species are overlaid to the fullest degree possible, to maximize efficiency

and increase AGFD’s ability to assess the effects of restoration treatments on

all forest wildlife.

Specific objectives of wildlife studies are to:

- describe the short-term effects of restoration treatments on forest habitat

characteristics;

- quantify short-term effects of restoration treatments on selected wildlife

species, including response variables describing diversity, abundance, reproduction, and habitat use;

- identify focal species and areas for long-term monitoring of treatment

effects on wildlife populations, communities, and habitats; and

- develop habitat management recommendations for future implementation of restoration treatments in southwestern forests.

Data are being collected at two scales, reflecting the home range sizes and

anticipated responses of focal species to the restoration treatments. For passerine birds and herpetofauna, a large number of individual home ranges have

been encompassed within individual treatment blocks, as well as the control

area. Consequently, detectable responses in population and community parameters may occur within these subunits, which is the focus of sampling.

Mule deer, Merriam’s turkey, and Kaibab squirrel have home ranges encompassing multiple treatment blocks and/or occur at relatively low densities on

the study area. For these species, responses to treatment are only detectable at

larger scale and may primarily be reflected in patterns of habitat use, rather

than population parameters. Sampling for these species is occurring over larger

aggregate units of treatment blocks.

Passerine bird studies

AGFD will extend the NAU passerine bird studies into the post treatment

phase. Point count and nest monitoring procedures conducted during the

pre-treatment phase in 1996 and 1997 will be repeated following treatments

in the area of the study for 2-3 years. The impacts of the treatments upon

avian abundance, diversity, and reproductive success will be analyzed. The

preliminary phase of this study is now complete and results are available in

Final Report—Heritage Grant I96008, Bird Abundance and Diversity Prior to

Restoration Treatments for Old Growth Ponderosa Pine.

Aerial photographs and GIS vegetative covers

In spring of 1997 low altitude, high resolution aerial photographs were

taken of the entire RCA. Cartographers at the Bureau of Land Management

National Applied Sciences Center will scan, ortho-photographically correct,

and prepare digital copies of the original photos. Once completed, AGFD will

incorporate these into a GIS Arc/Info data base for the Mt. Trumbull RCA.

AGFD will employ photo interpretation, site visits, and GIS overlays of NAU’s

300 m grid vegetation data to define and delineate the major vegetative cover

types present. This GIS information will then be applied to various wildlife/

vegetation studies in the RCA.

Moore, Davis, and Duck Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project
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Mule deer studies

Mule deer have been captured and radio-telemetered at Mt. Trumbull.

Following collection of field data, habitat selection analysis will be conducted.

Landscape scale habitat selection analyses will be conducted on the first two

years of data once aerial photographs for the area are entered into GIS.

Telemetered deer were located once monthly during the winter of 1998 to

learn where they travel during this season. General and micro-habitat data

collection components of the study are ongoing.

Kaibab squirrel studies

Tests of a track count index in ponderosa pine habitats on the Coconino

National Forest (Dodd 1996) have shown extremely high correlations (r=0.98)

between squirrel abundance and habitat use on the Mt. Trumbull restoration

study area. Count data is obtained from track plate arrays established in both

treatment and control areas. Each array consists of a randomly located grid of

track plates. Treatment and control arrays are sampled concurrently, for a 48

hour period, annually. Feeding and sign counts (fungi digs, cone cores, peeled

twigs, and terminal clippings) are counted in a sampling grid. To date, 6 sampling grids have been installed in untreated areas, and sign counts and population estimates have been completed for the areas of sampling.

Herpetofauna studies

These studies are being conducted to document the effects of forest restoration treatments on reptile species richness and relative abundance. Data collection relies primarily upon pitfall trapping. Pitfall arrays are overlaid on the

300 m ¥ 300 m grid system placed on the study area by NAU personnel. In

addition to pitfall arrays, plywood shaders will be placed on the ground in

conjunction with artificial PVC tubing burrows. Lizards caught or seen in the

arrays/substrates are included in data tallies. Data has currently been collected

at 76 pitfall arrays. Several arrays in the study area have never captured lizards,

suggesting that lizards are influenced by the varying habitat types present in

the RCA, and that certain current habitat conditions are not suitable for

them.

Migrant songbirds

The objectives of the migrating songbird study are similar to those of the

reptile study. AGFD is determining the current habitat relationships of autumn and spring migrant songbirds at the site through pre-treatment censusing.

Then, AGFD will evaluate the response of migrating songbirds to ponderosa

pine forest restoration at Mt. Trumbull. Pre-treatment data are being collected at 136 survey points, which are superimposed on NAU ecosystem monitoring plots. As treatment units are restored to pre-settlement conditions,

post-treatment surveys will be conducted at the same grid points. Seventynine bird species were recorded on the RCA during fall surveys.

Nestling provisioning rates and fledging rates

In 1998 AGFD initiated a study of parental food provisioning rates to nestlings for several bird species that are currently common breeders in the area,

and which may be affected by ecosystem restoration treatments. AGFD determined hatching dates, number of chicks, growth rates, food provisioning rates,

fledge dates and fledging success for western bluebird nests and white-breasted

Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project Moore, Davis, and Duck
130 USDA Forest Service Proceedings RMRS-P-29. 2003.

nuthatch nests. In addition, vegetation will be measured at nest sites for comparison with available habitat values.

Information on this subject is available in the report Short-term Wildlife

Responses to Ponderosa Pine Forest Restoration Treatments in the Mt. Trumbull

Area, Arizona, reports for 1997, 1998, and 1999.

BLM bat roost study

BLM’s primary role in this ecosystem restoration project is implementation

of operational treatments and assistance to NAU and AGFD studies where

necessary. However, BLM is conducting one bat roost study that will complement NAU and AGFD’s research.

Objectives of the Northern Arizona Bat Roost Inventory are to identify

and map key use areas for bats on BLM-administered public lands on the

Arizona Strip. Key use areas included roosts in trees, snags, caves, mines, cliff

faces, bridges, and culverts; watering areas at springs, stock tanks, and

catchments; and foraging areas. Priority was given to bat species of concern as

listed on the AGFD’s Wildlife of Special Concern in Arizona (AGFD 1997)

and the Arizona BLM Sensitive Species List (BLM 1998). Within a 15 mile

radius of the Mt. Trumbull Ecosystem Restoration project; the study was designed to:

- locate bat roosts in all habitat types;

- identify characteristics of trees/snags used as bat roosts;

- map the availability of suitable roost trees/snags;

- develop a model to predict which trees/snags are most likely to be occupied roosting sites during the active season;

- apply the model to control areas, pre-treatment, and treated areas within

the context of the Ecosystem Restoration Project;

- evaluate the impacts of ecosystem restoration treatments on bat populations and the availability of roost habitat;

- where possible, document roost-switching and/or evidence of roost site

fidelity; and

- evaluate the level of bat use of wildlife water developments in the area.

Future work will again focus primarily on Myotis volans in the Mt. Trumbull

area in a continuing effort to document the impacts to forest dwelling bats

from the ecosystem restoration project, and to develop the predictive model

for identifying potential roost trees/snags. A minimum of 20 additional snag

roosts will be required to lend statistical credibility to the sample size (Herder

and Jackson 1999).

Results from all wildlife project components will be compiled by AGFD

and project cooperators into a comprehensive final report. This report will

assess the short term effects of restoration treatments, and identify research

and management options for future treatments. Results will also be presented

at appropriate professional meetings and conferences, and submitted for publication in peer reviewed journals.

Conclusion

BLM intends to continue implementing the treatments in the Mt. Trumbull

and Mt. Logan area, while AGFD and NAU-ERI will continue their research

on the impacts of those treatments. Some units will receive second and third

burn and seed treatments. Land use planning for the newly designated Parashant

National Monument will provide new goals and objectives for the area. BLM

Moore, Davis, and Duck Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project
USDA Forest Service Proceedings RMRS-P-29. 2003. 131

fire and resources staff, along with our colleagues at NAU and AGFD, hope

to restore the ponderosa pine forest ecological integrity. Ultimately, our goal

is to allow wildfire to play its historic role in maintaining forest health.

Literature Cited

Altschul, J.H. and Fairley, H.C. 1989. Man, models, and management: An overview

of the archaeology of the Arizona strip and the management of its cultural resources.

Report prepared for USDA Forest Service and USDI Bureau of Land Management

by Statistical Research, Plateau Archaeology, Dames and Moore, Inc. Contract

#53-8371-6-0054.

Arizona Game and Fish Department. 1997. Wildlife of special concern in Arizona.

Arizona Game and Fish Department. Phoenix, AZ.

Bureau of Land Management. 1998. Arizona BLM sensitive species list. Bureau of

Land Management, Arizona State Office. Phoenix, AZ.

Dodd, N.L. 1996. Tassel-eared squirrel research project: April 1996 trapping results

assessment. Arizona Game and Fish Department, Research Branch, Phoenix, AZ.

Unpubl. Rep.

Covington, W.W., P.Z. Fule, M.M. Moore, S.C. Hart, T.E. Kolb, J.N. Mast, S.S.

Sackett, and M.R. Wagner. 1997. Restoring ecosystem health to ponderosa pine

forests of the southwest. Journal of Forestry. 95:23-29.

Covington, W.W., and M.M. Moore. 1994. Southwestern ponderosa forest structure:

Changes since Euro-American settlement. Journal of Forestry. 92: 39-47.

Figure 2—Post treatment example from Mt. Trumbull.

Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project Moore, Davis, and Duck
132 USDA Forest Service Proceedings RMRS-P-29. 2003.

Moore, Davis, and Duck Mt. Trumbull Ponderosa Pine Ecosystem Restoration Project

Harrington, M.G. and S.S. Sackett. 1988. Using fire as a management tool in

southwestern ponderosa pine. Panel paper presented at the conference, Effects of

Fire in Management of Southwester Natural Resources. Tucson, AZ.

Herder, M.J. and J.G. Jackson. 1999. Impacts of the Mt. Trumbull ecosystem

restoration project on forest dwelling bats. Preliminary report. Bureau of Land

Management, Arizona Strip Field Office.

Mast, J.N., P.Z. Fule, M.M. Moore, W.W. Covington, and A.E.M. Waltz. 1999.

Restoration of presettlement age structure of an Arizona ponderosa pine forest.

Ecological Applications. 9(4): 228-239.

National Park Service, Western Region. 1992. Western region fire monitoring

handbook. National Park Service publication. San Francisco, CA.

Northern Arizona University. 1997. Mt. Trumbull Ecosystem Restoration Project -

Progress Report, April 1997. Forest Ecology Lab, School of Forestry, College of

Ecosystem Science and Management, Northern Arizona University. Flagstaff, AZ.
USDA Forest Service Proceedings RMRS-P-29. 2003. 133

Wildlife Responses to Alternative Fire

Management Treatments: The National Fire/

Fire Surrogate Study Approach

Steve Zack1 and Kerry Farris2

The National Fire/Fire Surrogate Study involves parallel studies with

common treatments and research protocols at 13 sites across the country, with most sites in the West in ponderosa pine dominated forests. All sites

are applying three replicates each of three treatments (prescribed fire alone,

thinning then fire, and thinning alone [the “fire surrogate”] plus a control

(continued fire suppression). The wildlife study we are directing of this effort

emphasizes understanding the responses of songbirds, woodpeckers, and small

mammals to these treatments. Particular emphasis is placed on evaluating the

foraging response to fire treatments of woodpeckers and other “bark-gleaner”

(nuthatches, creepers, etc.) birds. General hypotheses of predicted wildlife

responses have been developed and will be tested with a national meta-analysis of

results. Early results from our NE Oregon site will be presented, and the

context and constraints of this novel, national effort will be presented.

1Wildlife Conservation Society,

Portland, OR.

2Wildlife Conservation Society, Bronx,

NY.
134 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 135

Comparing Two Methods of Identifying

Ecological Restoration Opportunities

Jimmie D. Chew1

Abstract—Two methods for identifying ecological restoration opportunities in the

Northern Region of the Forest Service are compared. Different analysis methods are

often used to address issues at different planning scales. The first method is a nonspatial

characterization of current vegetation conditions using Forest Inventory and Analysis (FIA) plot data grouped by potential vegetation for both the state of Montana and

smaller landscape areas. The second method uses a spatially explicit model on two

landscapes of one-half million and 1 million acres. Similar rule sets are used in both

methods to compare current vegetation condition to historic vegetation conditions.

This analysis indicates that spatially explicit modeling for determining restoration

opportunities would not always support the same decisions made through a nonspatial

analysis. For both types of land on the Pintler District area, suitable and unsuitable,

the spatially explicit modeling indicates a greater need for more costly restoration

and conversion treatments and less maintenance treatments compared to FIA plots

for the entire state and just those within the area. For the three land classes on the

Bitterroot Face area, only the suitable land had a similar mixture of treatment needs

compared to the FIA plots for the entire state and those within the area. The differences in the levels of the treatment opportunities between the methods and scales

can have a significant impact on the level of treatment needs and associated budgets

identified for programs.

Introduction

The departure of current vegetation from the conditions maintained by

historic fire regimes has had an impact on our ability to meet many

management objectives. Increases in the activity of bark beetles, root diseases,

and defoliators are examples of insects and pathogens capitalizing on a large

food base of susceptible host trees. Increases in fire severity and fire sizes are

considered a result of the lack of fire disturbance across landscapes (Monnig

and Byler 1992).

Identifying the level of treatment opportunities and the types of land that

they occur on is an essential step in identifying funding to meet management

objectives. To identify the level of management opportunities that would be

needed to restore historic vegetation conditions, the Northern Region of the

US Forest Service developed a procedure to analyze the Montana portion of

Forest Inventory and Analysis (FIA) data (working paper on file with Region

Office, Region One). This approach is based on a set of rules for comparing

current vegetation conditions on plots with historic fire regimes. The second

approach also uses similar rules to make a comparison, but it utilizes a spatially

explicit landscape scale model, developed at the Rocky Mountain Research

Station. The model is used to project the trend of current inventoried vegetation and to create a representation of historic vegetation. This paper compares

the results of these two approaches.

1USDA Forest Service, Rocky Mountain

Research Station, Missoula, MT.
136 USDA Forest Service Proceedings RMRS-P-29. 2003.

Treatment opportunities used for both methods were categorized in three

condition classes: maintenance, restoration, and conversion.

• Maintenance opportunities are identified when there is little or no change

required in forest cover type or density. Maintenance treatments are relatively low cost operations such as deferring treatment for greater than

two decades or using wildland prescription fire for resource benefit with

no pre-treatment of fuels.

• Restoration opportunities are identified when there is little need to change

the forest cover type but the density needs to be changed. When the

associated vegetative conditions are compared to management objectives,

pre-treatment such as thinning is required prior to the use of wildland

prescription fire.

• Conversion opportunities are identified when the current cover type is

substantially different from historic conditions. Conversion opportunities have higher investment costs such as replacing a current cover type

due to its relative departure from historical conditions. Treatments of

regeneration harvests or stand replacing fire followed by reforestation of

intolerant species such as ponderosa pine, western larch rust resistant

western white pine, whitebark pine, or aspen are needed to return to

historical conditions.

FIA Plot Analysis Method

The Forest Inventory and Analysis is a national, strategic scale program

conducted on a state by state basis with a set of field sample locations distributed with approximately one sample location (FIA plot) every 6,000 acres

(Gillespie 1999). Treatment condition classes were assigned to the set of

Montana plots based on the potential vegetation setting with an inferred historical fire regime from the literature (Hardy et.al. 1998). Table 1 is an example

of the condition class rule sets or assignments given by historic fire regimes.

The current cover type existing on a potential vegetation group (PVG) setting

was determined by species with a plurality of basal area. A current structure

class was determined: seedlings/saplings are <5 inches dbh; poles are 5-8.9

inches dbh; medium is 9-14.9 inches dbh; large is 15-20.9 inches dbh; and

very large is >21 inches dbh. S is single story, 2S is two stories, and multi is

three or more stories. A current density class was assigned: L is 10-39%, M is

40-69%, H is >70%. Finally, a treatment class was determined: A maintenance

class (M) is assigned if the current cover type was consistent with the historical

fire regime within the PVG setting, indicating relatively low departure from

historical conditions. A condition class of restoration (R) was assigned if departure was not great (density or layering increased, but cover type had not

changed). A conversion class (C) was assigned if a greater departure from

historical conditions was indicated by an increase in density and a change of

cover type. All three classes are treatment opportunities with different tools

and costs associated as in the descriptions above.

Plots were stratified into land classes. The three land classes used for this

comparison are suitable, non-suitable, and wilderness. The suitable and unsuitable classes refer to the suitability of National Forest land for the production

of commercial timber products. Only forested FIA plots on National Forest

System Lands were used in this analysis. A total of 2,343 plots were used for

all of Montana, 94 for the Pintler District, and 43 for the Bitterroot Face.

Chew Comparing Two Methods of Identifying Ecological Restoration Opportunities
USDA Forest Service Proceedings RMRS-P-29. 2003. 137

Landscape Modeling Approach

The modeling system used is SIMPPLLE (Simulating Pattern and Process

at Landscape Scales) (Chew 1995, 1997). SIMPPLLE uses stochastic disturbance processes to model vegetation change. Vegetation is described by cover

type, size and structure class, and density classes. Long-term simulations (300-

500 years) that start with the current vegetation conditions are made without

fire suppression to re-create a representation of historic conditions for a specific landscape. Multiple simulations are used to create the frequency of

occurrence for the cover type attributes. Multiple short-term simulations

(50 years) with fire suppression are also made on the inventoried current

vegetations conditions to generate frequency of future vegetation conditions.

Similar rules sets are used to compare the simulated historic vegetation conditions with the simulated current vegetation conditions trends.

If there is no difference between these simulated vegetation conditions, the

treatment class of maintenance is assigned. If the two most frequently occurring cover types from the simulated current trends are consistent with modeled

historic cover types, but either density or size class differs, the restoration class

is assigned. If the cover type does not match between the two sets of simulations, regardless of the other two attributes, the conversion class is assigned.

Table 1—Treatment class for historical fire regimes of 35-100+ years, mixed severity. See test for

explanation of abbreviations.

Structure Density Treatment

Existing cover type/group/a class/b class/c class

Limber pine (Pinus flexis) single, two story L M

Limber pine multi M-H R

seedling/sapling M-H R

pole M-H R

medium M-H R

large M-H R

very large M-H R

Ponderosa pine (Pinus ponderosa) S,2S L M

Ponderosa pine S,2S,multi M-H R

seedling/sapling M-H R

pole M-H R

medium M-H R

large M-H R

very large M-H R

Eastside Region One DF (Pseudotsuga menziesii):

Eastside Region One DF (xeric) S,2S L M

Eastside Region One DF (xeric) <25% S,2S,multi M-H R

Eastside Region One DF (xeric) >25% S,2S,multi M-H M

seedling/sapling M-H R

pole M-H R

medium M-H R

large M-H R

very large M-H R

Lodgepole pine (Pinus contorta) <80 years all All M

Lodgepole pine (upper subalpine) >80 years all All M

Comparing Two Methods of Identifying Ecological Restoration Opportunities Chew
138 USDA Forest Service Proceedings RMRS-P-29. 2003.

Comparison

The comparison is made using the percents of the condition classes for all

of the Montana FIA plots, percents for the FIA plots within the two landscapes, and the percents from the SIMPPLLE modeling of two landscapes.

Existing vegetation conditions on the Bitterroot Face on the Bitterroot National Forest is described by field inventories. The Pintler Ranger District from

the Deerlodge Forest is described by satellite imagery. The comparison of the

percent of treatment opportunity class on suitable land for the Pintler District

is in figure 1. The comparison for the suitable land on the Bitterroot Face is in

figure 2. For the nonsuitable land, figure 3 describes the Pintler District landscape and figure 4 describes the Bitterroot Face. For the wilderness land, figure

5, only the wilderness portion of the Bitterroot Face landscape was used.

For all except the suitable land class on the Bitterroot Face, there is less

maintenance treatment opportunity class identified by the modeling approach.

To gain insight into what accounts for the difference, individual plant communities from the Pintler District landscape are examined. These communities

were selected from one type to use for the comparison because of the simplicity

Figure 1—Comparison of percent of

suitable land by treatment

opportunity for the Pintler District.

Figure 2—Comparison of percent of

suitable land by treatment

opportunity for the Bitterroot Face

landscape.

Chew Comparing Two Methods of Identifying Ecological Restoration Opportunities
USDA Forest Service Proceedings RMRS-P-29. 2003. 139

Figure 3—Comparison of percent of

nonsuitable land by treatment

opportunity for the Pintler District.

Figure 4—Comparison of percent of

nonsuitable land by treatment

opportunity for the Bitterroot Face

landscape.

Figure 5—Comparison of percent of

wilderness land by treatment

opportunity for the Bitterroot Face

landscape.

Comparing Two Methods of Identifying Ecological Restoration Opportunities Chew
140 USDA Forest Service Proceedings RMRS-P-29. 2003.

within the Rule Sets (table 1). Xeric Douglas-fir, on slopes greater than 25%,

all medium size class, and medium to high density are always put in the restoration condition class. This means that the cover type does not need to be

changed but some change in density and structure may need to be made.

Within the Pintler District, using the modeling approach, the Douglas-fir

cover type, medium size class, and density 3 on xeric sites have only 2% classified as restoration. The conversion treatment class was assigned to 98%. The

difference in results from the SIMPPLLE modeling approach are from being

spatially explicit. The arrangement of different cover types present in the current landscape has an impact in the multiple simulations that are made to

represent both current trends and possible historic conditions. Different plant

communities can be in the same PVG and be assigned the same historic fire

regime, but be surrounded by different cover types. The timing of fire events

with the size class development of the different cover types and the presence

or absence of seed sources all play a part in determining how cover types can

vary from one fire event to another.

Figure 6 is taken from a portion of the Pintler District and displays a gradient of Douglas-fir stands intermixed with nonforest communities, to solid

Douglas-fir, to Douglas-fir adjacent to lodgepole pine. Three plant communities are selected for a comparison.

Figure 6—Map showing three plant communities selected for detailed examination of modeled results.

Chew Comparing Two Methods of Identifying Ecological Restoration Opportunities
USDA Forest Service Proceedings RMRS-P-29. 2003. 141

From the modeling method, plant community 37193 is assigned a conversion treatment need. In the modeled current trend, the plant community stays

the same type and size class over the next five decades, but increases in density.

Historic conditions have the two most dominant cover types being uplandgrasses and lodgepole pine. With lodgepole pine it did not grow out of the

seedling sapling stage before it burned again. The high level of fire in historic

simulations minimizes the acres of the Douglas-fir cover type. Acres of both

upland-grasses and lodgepole pine are expanded.

The modeling method resulted in plant community 37711 also being assigned a conversion need. The current trend simulations have the plant

community staying the same cover type and size class but increasing in density

over the next five decades. Historic conditions have the plant community

always in shrub cover type. The level of simulated historic fire has the nonforest

communities occurring in areas that have been invaded by Douglas-fir.

Plant community 36814 is assigned a restoration need. The simulated current trend has the plant community staying Douglas-fir and size class medium

but increasing in density. The simulated historic conditions are always a Douglas-fir cover type but the size class is predominately pole with frequently

becoming pole two-story. The density stays class 3 ninety percent of the time.

Discussion

The FIA process for the entire state was intended to be a broad, nonspatial

classification of treatment opportunities appropriate for a Regional assessment.

The method of using a spatially explicit model such as SIMPPLLE is often

considered more appropriate at the landscape scale for Forest or District planning. Planning processes are often considered hierarchical, with the results of

finer level, more detailed planning expected to fall within the context of the

decisions made at broader assessment scales. The types of decisions made using a broad scale analysis would continue to be supported as more detailed

analysis and planning are conducted. However, this comparison indicates that

spatially explicit modeling for determining restoration opportunities would

not always support the same decisions made through a nonspatial analysis. For

both types of land on the Pintler District area, suitable and unsuitable, the

spatially explicit modeling indicates a greater need for more costly restoration

and conversion treatments and less maintenance treatments compared to both

the FIA plots for the entire state, or those within the area. For the three land

classes on the Bitterroot Face, only one, the suitable land, had a similar mixture of treatment needs compared to either the FIA plots for the entire state

or those within the area. Would budgets and programs developed for restoration treatments needs from the broad nonspatial analysis underestimate the

needs for restoration and conversion? Or are the needs for restoration and

conversion overestimated from the spatial analysis that may be completed at

other planning levels?

The answer to these questions depends on the confidence in the analysis

method used. Does the spatially explicit modeling system overestimate how

fast current conditions may change? Does the modeling method overestimate

the variability that existed historically? Or does the use of historic fire regimes

by PVG’s underestimate the historic variability? The differences we see in this

comparison can raise additional questions about differences in assessment

methods used at different planning scales. Where broad, nonspatial analyses

are made, should they be adjusted by using “sample areas” that are modeled

Comparing Two Methods of Identifying Ecological Restoration Opportunities Chew
142 USDA Forest Service Proceedings RMRS-P-29. 2003.

spatially? Or should broad scale assessments be composed of spatially explicit

analyses on a size of areas that can be added to represent the total area of

interest?

References

Chew, J.D. 1995. Development of a system for simulating vegetative patterns and

processes at landscape scales. Missoula: University of Montana; Ph.D. dissertation.

182 p.

Chew, J.D. 1997. Simulating vegetation patterns and processes at landscape scales.

Pages 287-290. In Proceedings of Eleventh Annual Symposium on Geographic

Information Systems, Integrating Spatial Information Technologies for Tomorrow.

1997 Feb 17-20; Vancouver, British Columbia, Canada. Published by GIS World.

640p.

Gillespie, Andrew J. R. 1999. Rationale for a national annual forest inventory program.

Journal of Forestry. 97(12): 16-20.

Hardy, C.C.; J.P Menakis, D.G Long, J.K Brown, D.L. Bunnell. 1998. Mapping

historic fire regimes for the Western United States: Integrating remote sensing and

biophysical data. In: Proceedings of the 7th Biennial Forest Service Remote Sensing

Applications Conference. 1998 April 6-9; Nassau Bay, TX. Bethedsa, MD: American

Society of Photogrammetry and Remote Sensing. 288-300.

Monnig, E. and J. Byler. 1992. Forest health and ecological integrity in the Northern

Rockies. FPM REPORT 92-7, USDA Forest Service, Missoula, MT.

Chew Comparing Two Methods of Identifying Ecological Restoration Opportunities
USDA Forest Service Proceedings RMRS-P-29. 2003. 143

The Post-Burning Response of Bark Beetles to

Prescribed Burning Treatments

David J. Ganz1, Donald L. Dahlsten1, and Patrick J. Shea2

Abstract—Ecologists and fire scientists have recommended reintroducing fire in firedependent ecosystems to achieve the twin goals of restoring pre-settlement forest

conditions and reducing catastrophic fire risk (McKelvey 1996, Parsons 1995). Early

work by forest entomologists (Miller 1927, Miller 1960; Rasmussen et al. 1996, Salman

1934) established a direct relationship between fire injury and subsequent insect

attack in burned-over areas. Initial concern has centered on the primary tree killers

Dendroctonus spp. and Scolytus ventralis LeConte. This research is also finding that

Dendroctonus valens and Ips pini are causing tree mortality with both fall and spring

prescribed burns. Post-burning bark beetle induced mortality can be quite significant as demonstrated by two case studies presented here from Lassen Volcanic

National Park and Spooner Summit, Lake Tahoe Basin Management Unit. From these

two sites, inferences are made on the effect of seasonality for predisposing trees to

particular bark beetles. In comparing these two populations, there was no significant

difference in the mean number of trees killed by insects in each seasonal window. As

case studies, not enough replicates existed to merit a more rigorous analysis. As such,

management implications of post-burning bark beetle response are discussed given the

information available on fire-insect interactions during these two seasonal windows.

Introduction

Bark beetles are recognized as a significant factor in California forest

ecosystems (Bradley and Tueller 2001, Mutch 1994). Susceptibility of

forest stands to bark beetle attack has increased through combined effects of

climate change, past forest management, fire suppression, drought, and other

pests. The accelerated use of prescribed fire in California ecosystems may or

may not alleviate these conditions. Hence, an increased comprehension of

fire-insect interactions is necessary for more effective science-based management of forest ecosystems (McCullogh et al. 1998).

Fire may increase the risk of tree mortality from bark beetles by compromising tree defenses against beetles (Geiszler et al. 1984). Charring of the

lower bole may damage the tree’s vascular cambium, and provide large areas

for bark beetle attack by rupturing the resin ducts that help defend pines from

attack. Bark beetles, which bore through the tree’s outer bark to feed and

reproduce within the phloem, are inhibited by oleoresin, resulting in negative

relationships between resin production and beetle attack success (Coyne and

Lott 1976). Oleoresin is produced by specialized epithelial cells within the

xylem and stored within vertical resin ducts in the xylem and in bark resin

canals. As a result of heat trauma to these tissues, oleoresin production and

defense may be reduced in the lower bole. Alternatively, it has been suggested

that some trees which have a long evolutionary history of interactions with

fire and bark beetles respond to fire with increases in resin flow to counteract

the increased risk of bark beetles (Feeney et al. 1998). Contrary to earlier

1Department of Environmental Science,

Policy and Management, University of

California, Berkeley, CA.

2USDA Forest Service, Pacific Southwest

Research Station, Davis, CA.
144 USDA Forest Service Proceedings RMRS-P-29. 2003.

works, it has been reported that increased resin flow does not necessarily result in a decrease in insect activity following fire (Santoro et al. 2001).

Prescribed fire treatments applied in the spring and fall in immature ponderosa pine stands have resulted in significantly different mortality (Harrington

1993). Fires were ignited in the late spring, midsummer, and autumn. Mortality of trees scorched in the spring and summer was about 2.5 times greater

than the autumn for similar crown damage. Most trees greater than 18 cm

diameter at breast height (dbh) survived injury even with greater than 90%

crown scorch. Following spring and summer injury, trees smaller than 10 cm

dbh with greater than 50% crown scorching died, but about 90% crown scorch

was required to kill large trees. Differences in mortality within the two

seasonal windows are likely due to contrasts in physiological activity and to

carbohydrate storage (Harrington 1993). Physiological activity was greatly

reduced at the time of autumn burns in response to short day lengths, cool

air, and cool soil temperatures (Fritts 1976, Kozlowski et al. 1991). Dormant

protected buds are likely to survive within scorched crowns and will produce

new foliage the following spring (Ryan 1990).

The best indicator of crown injury appears to be the proportion of the

crown scorched or killed by fire (Peterson 1985, Ryan 1982, Ryan et al. 1988,

Ryan and Reinhardt 1988, Wagener 1961). Empirical evidence has shown

that over a wide range of conditions, mortality increases with the square of the

fraction of the crown killed (Ryan 1990). Other factors that can be used in

mortality prediction modeling include dbh, species, scorch height, bark char

height, and local fuel consumption.

Objectives of Study

The Forest Service is looking to use prescribed fire as a tool to reduce the

fire hazard and improve the physiological condition of the ecosystem. Jack

Ward Thomas, former Chief of the USDA Forest Service, asked the agency to

“increase mechanical and prescribed fire treatments to 3,000,000 acres per

year in fire dependent ecosystems by the year 2005” (Thomas 1995). The

National Fire Plan has since stated that Federal agencies will “jointly develop

programs to plan, fund, and implement an expanded program of prescribed

fire in fire-dependent ecosystems.”1 This policy has led to a significant scientific commitment to understanding the impacts of prescribed fire operations.

The purpose of this paper is to compare the extent of tree mortality and

bark beetle-induced mortality from two different prescribed fires used in the

fall and spring burning windows. The goal is to provide forest managers in

this region with an idea of the severity of tree mortality associated with prescribed fires burned under two cases with some similarities in environmental

and fuel conditions. In addition, management implications of post-burning

bark beetle response are discussed given the information available on fireinsect interactions during these two seasonal windows.

Study Sites

Lassen Volcanic National Park Roadside Unit Spring Burn

The Lassen Volcanic National Park Roadside burn is located 18 km from

Manzanita Lake, about 100 m from Lost Creek Campground. The exact

Ganz, Dahlsten, and Shea The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments

1 National Fire Plan Website http://

www.fireplan.gov/.
USDA Forest Service Proceedings RMRS-P-29. 2003. 145

location within the park is T. 31 N., R. 4 E., Sec. 10, NE o (figure 1). The

Roadside burn is 102 acres and includes all aspects at elevations of 1725-1798 m

(5660-5900 ft).

The objectives of the spring burn were to reduce hazard fuels, monitor fire

effects, and restore fire as a natural process. In addition, the hope was to kill a

large number of white firs to eventually initiate more pine regeneration. Two

permanent square plots were set up using fuels sampling protocol in Western

Region Fire Monitoring Handbook (1992). One plot is located in the white

fir-dominated portion and the other in ponderosa pine-dominated portion of

the spring burn. A total of 93 trees with permanent tags were revisited over

the three years since 1999. In the fir-dominated plot, white fir represents at

least 60% of the composition of overstory species. Other dominant trees are

ponderosa pine, Jeffrey pine, and incense cedar. Sugar pine snags are located

in the burn but only in small numbers and do not show up in overstory sampling. In the pine-dominated plot, the overstory species is predominantly ponderosa pine, and together with white fir, these two comprise 80% of the

overstory with the remaining component incense cedar and Jeffrey pine.

The precipitation at Lassen Volcanic National Park falls primarily during

November through May in the form of snow and rain. Annual air temperatures have an average 14 ̊C as the daily high and as the low as 0 ̊C. Annual

The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments Ganz, Dahlsten, and Shea

Figure 1—Site Locations within California.
146 USDA Forest Service Proceedings RMRS-P-29. 2003.

total precipitation averages 1067 mm (42 in) per year and snowfall 4800 mm

(188 in) per year (Manzanita Lake Weather Station 1949-2002). Relative

humidity is fairly high in the spring (during the burning window), ranging

from 25% to 85%.

Spooner Summit

Spooner Summit is located in the Lake Tahoe Basin Management Unit,

approximately 35 km northeast of South Lake Tahoe (figure 1). Spooner Summit is a steep montane environment covering 50.6 hectares (125 acres) and

ranging from 1981 to 2176 meters (6500-7140 ft). Spooner Summit makes

up part of the Lake Tahoe’s forests that were last cut 140 years ago to supply

fuel and timbers for mining the Comstock Lode near Virginia City, Nevada

(Taylor 1998). In the early 1900s, fire eradication on federal land became

mandatory with state government following suit with the enactment of the

California Forest Protection Act in 1905. The regrown forests are now 120

years old and are two to 10 times denser than the original forests (Taylor

1998). These trees now compete intensely for resources, especially water.

Drought, combined with the absence of fire which thins forests, has predisposed the trees to bark beetle attack. Under such a scenario, thinning and the

reintroduction of fire have been suggested.

In 1992, Forest Pest Management (of Region 5 of the Forest Service) initiated a study to evaluate the effectiveness of Jeffrey pine beetle suppression

by comparing the number of trees killed by Jeffrey pine beetle in areas where

infested trees were removed annually (treated areas) with the number of beetlekilled trees in areas where the infested trees were not removed (untreated

areas). A summary of preliminary results to date indicate that, for selected

areas in Lake Tahoe Management Unit between 1993 and 1995, mortality

was reduced by 87% in the treated areas, and mortality in the untreated areas

increased 182% (table 1). These findings led to the helicopter logging of

Spooner Summit in 1996 down to densities averaging 222 trees per hectare

(90 per acre).

Table 1—USDA Forest Service Forest Pest Management study on bark beetle infested Jeffrey pines in

the Lake Tahoe Basin Management Unit, 1992–1995.

1

Status and location 1992-93 1993-94 1994-95 Increase/decrease

Treated

Nevada Beach and Zephyr Cover 909 trees 240 trees 121 trees -87%

Untreated

Spooner Summit 1213 trees 1966 trees 2213 trees +182%

1

Forest Pest Management website: http://www.r5.fed.us/fpm/fh_94-95/m261e.htm.

Spooner Summit’s climate varies from average high temperatures in July of

26  ̊C to lows of 2  ̊C in January. Annual rainfall is very low with an average of

212 mm and average annual snowfall is 5500 cm (216 in). Slopes are very

steep, ranging from 10% to 50%. Soils are rocky with shale-like volcanic parent

material. A total of 235 trees were tagged and measured. Average tree dbh is

33.6 cm (SD =17.5, CI (95%) = .08) and average tree height is 12.8 m (SD=

4.2, CI (95%) = .09). Pre-fire fuel loads averaged 79 tons/hectare. Ceanothus

prostratus dominates the foliar cover of the understory.

Ganz, Dahlsten, and Shea The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments
USDA Forest Service Proceedings RMRS-P-29. 2003. 147

Site Differences

Two distinctive sampling units, using the Park’s Fire Monitoring system,

stratified the Roadside burn as Pine and Fir units. Although the species composition, precipitation, location, and management histories are quite distinct,

the elevation, size of burn, and forest structure of Pine unit was similar to

Spooner Summit making the Roadside spring burn a suitable comparison (table

2). These environmental factors may play a stronger role than seasonality;

therefore, in comparing these two cases, only inferences can be made as to

why these populations (and their fire-insect interactions) differ.

Table 2—Comparison of metadata from Lake Tahoe Basin Management Unit’s Spooner

Summit Fall Burn and Lassen Volcanic National Park’s Roadside Unit Spring Burn. Preburn data collected in 1996 for Spooner and 1997 for Roadside.

Spooner Summit Roadside Unit, LNP

Size of burn 50.6 ha 41.3 ha

Month and year of burn October 1996 June 1999

Elevation 1981-2176 m 1725-1798 m

Avg. temperature (high) 26  ̊C 14 ̊C

Avg. temperature (low) 2  ̊C 0 ̊C

Snowfall 5500 cm 480 cm

Precipitation 212 mm 1067 mm

Pre-fire stocking 222 trees/ ha 300 trees/ ha

Average DBH 33.6 cm 37.3 cm

Species composition 98% Jeffrey pine Fir - 60% Abies concolor

Pine - 73% Pinus ponderosa

Methods

Fire Effects Monitoring

Pre-burn surface fuel loads at Lake Tahoe Basin Management’s Spooner

Summit were determined using a grid of 28 plots. Plot centers were placed

systematically on 4 transects that were installed directly upslope in the Spooner

Summit fall prescribed fire unit. At each of the 28 plot centers, surface fuels

were inventoried using three Brown (1974) transects at random azimuths

(total of 84 transects installed). One and 10 hour fuels were sampled from

0-2 meters, 100 hour fuels from 0-3 meters, and 1000 hour fuels from 0-10

meters. At 10 randomly located grid points, all trees greater than 5.9 inches

(15 cm) dbh within a one acre circular plot were tagged, identified to species

and dbh and heights measured (dbh to the nearest 0.1 cm and height to the

nearest 0.1 m).

Plot locations at Lassen Volcanic National Park’s Roadside were selected

utilizing a stratified random sampling design. Data were collected within two

20 m ¥ 50 m plots pre-fire, immediately post-fire, and 1- and 2-years postfire. All overstory trees ≥5.9 inches (15 cm) dbh were recorded within these

two plot areas while pole trees between 1.0-5.9 inches (2.5-15 cm) dbh were

sampled within one 10 m ¥ 25 m quarter of the plot. All sampled trees were

The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments Ganz, Dahlsten, and Shea
148 USDA Forest Service Proceedings RMRS-P-29. 2003.

tagged, mapped, identified to species, and recorded as live or dead in accordance with Western Region Fire Monitoring Handbook protocols (1992).

Fuel load was measured along four 15.2 m (50 ft) transects per plot using the

planar intercept method (Brown 1974). Woody fuel load includes: 1-hour

(0-0.24 inches or 0-0.63 cm in diameter), 10-hour (0.25-0.99 inches or

0.64-2.53 cm) 100-hour (1.0-2.99 inches or 2.54-7.61 cm), and 1000-hr

(≥3 inches or ≥7.62 cm) fuels. Total fuel load also includes duff, which consists of the layer of partially decomposed, consolidated organic matter below

the litter layer. Litter, which is defined as the freshly cast organic matter still

retaining its morphological characteristics, was measured but is not included

in the total fuel load calculation. Data were analyzed utilizing the Fire Monitoring Software version 3.0. This software provides a platform for data entry

and storage while also performing functions including minimum plot calculations and analyses of change over time.

At both of these prescribed burning locations, four duff pins were placed

(using the four cardinal directions) around the boles of randomly chosen trees

at 30.5 cm (1 ft) from the main stem. Three to five trees were chosen at

randomly chosen distances and azimuths (from grid point centers) using a

table of random numbers. These trees were thus geo-referenced to plot centers for post-fire visits. Duff stakes were driven flush with the top of the duff

and then the fine fuels consumed were measured following the burn. These

marked trees served as a subsample of the population for a closer analysis of

associated damage to the root collar and D. valens LeConte concentrations

around the injuries.

The maximum height of bark charring was recorded on all trees greater

than 5.9 inches (15 cm). Ocular estimates of three codes of bark char were

recorded to the nearest 0.1 of a meter. The maximum height of each charring

code was measured on the highest fire-charred side and then again on the

opposite side of the tree. If the bark was uncharred, then a zero was recorded.

The three levels of charring, designed for use with a similar study at Blacks Mountain Experimental Forest, Lassen County, are defined as follows (Oliver 2000):

• Code 1-Bark is black but not consumed and the bark fissures are not black.

• Code 2–The entire bark including the bark fissures is black, but the bark has

not been consumed by fire.

• Code 3–The entire bark is black including the fissures and a significant

degree of bark consumption is evident. Bark consumption often is indicated by a “smoothing” of the original bark profile of ridges and fissures.

Four crown measurements were taken to determine the percentage of live

crown scorched. These four measurements are defined as follows:

• Measure the height above ground to the base of the living crown if the

crown is unscorched or to the base of the crown that was living before the

burn but is now dead, in the case of crown-scorched trees.

• Measure the height above ground to the base of the crown that had scorched

needles but is still living.

• Measure the height above ground to the base of the unscorched portion of

the crown.

• Measure the crown width.

Using these four measurements, the overall percentage of scorched crown

was estimated. Overall crown surface area (Ca) is calculated assuming the crown

is a solid geometric shape with a measured crown depth (L) and crown width

(D). In this study, the amount of crown scorched assumed that the crown

resembled a conoid shape. The equation used for crown surface area is as follows:

Ganz, Dahlsten, and Shea The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments
USDA Forest Service Proceedings RMRS-P-29. 2003. 149

2 2

)

2

* ( 2

D L D Ca = +

p

For a comparison of burning effects on tree mortality compared to nonburned splits, a paired T-test was used to evaluate the two normally distributed populations. This statistical test was also used to compare a difference in

means, in this case the difference of mean mortality between burned and nonburned populations, or between spring and fall burning treatments. It assumes the following hypothesis:

Ho : md = 0 and HA : md π 0 or also expressed HA : md πmo

It was particularly useful in these two cases where there was not adequate

replication in the design although inferences were made through comparing

the two populations.

Insect Activity Monitoring

Post-burn visits to each plot were performed in the late fall of each year

following the burn to determine bark beetle-induced mortality. Spooner Summit performed in 1996 was revisited from 1997 to 2001 and Roadside spring

burn performed in 1999 was visited from 1999 to 2001. At both locations,

permanent control plots were set up in non-burned areas to compare the effect of burning itself on insect activity. For this comparison, 112 unburned

trees, 50 at Roadside and 62 at Spooner Summit, were monitored for the

duration of the study.

For the spring burn, multiple visits throughout the growing season were

performed to determine the insect invasion pattern for D. valens. For this

insect, the number of attacks as evidenced by large red pitch tubes and/or

granular frass at the root collar was counted and woodpecker foraging noted.

The presence of frass, gallery patterns and/or woodpecker activity was used to

determine activity of other Dendroctonus beetles, mainly Jeffrey pine beetle

(D. jeffreyi Hopkins), mountain pine beetle (D. ponderosae Hopkins), and

western pine beetle (D. brevicomis LeConte). Woodpeckers working on trees

infested by D. brevicomis shave the bark to a more or less uniform thickness

(Otvos 1965). This enables a visual identification decipherable from the other

Dendroctonus species. Other insects like the pine engraver (Ips pini Say) as

well as the California flatheaded borer, Melanophila californica Van Dyke,

have a pronounced role in mortality after fire (Lyon 1970) and these insects

were identified by intrusive sampling (bark removal) of the bole when outside

verification was not feasible. The role of these insects and other typical secondary infestation beetles were documented in this project. Scolytus ventralis

LeConte activity was indicated by white fir pitch streamers and top kill. Within

white fir, mortality from Tetropium abietis Fall was also documented. A major

assumption of this study is that phloem-feeding insects will not infest trees

with no viable cambium. Therefore a distinction is made by those trees killed

by fire and those predisposed by fire and subsequently killed by insects.

Results

Fire Monitoring

Although burned in different seasons, both Spooner Summit and Lassen

Volcanic National Park Roadside prescribed burns have shown similar pre-fire

The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments Ganz, Dahlsten, and Shea
150 USDA Forest Service Proceedings RMRS-P-29. 2003.

fuel loads and standard deviations (table 3). In comparing the averages from

the 235 burned trees of Spooner Summit and the 88 burned trees of Roadside, the fire severity measures of bark char and crown scorch are remarkably

similar (table 3). Although the standard deviations for all of these averages are

quite large, the ranges follow a similar trend in both burns.

The Lassen Volcanic National Park Roadside Unit has two monitoring plots:

one within a white fir-dominated area and the other within a ponderosa pinedominated area. The white fir-dominated portion of this prescribed burn suffered only mild fire severities with scorching averaging 36% of live crown and

bark char (Code 1) reaching heights of 2 m on the high side and .75 m on the

low side. Unlike with the pine species, bark char is probably a better severity

measure than crown scorch for predicting mortality in white fir.

The ponderosa pine-dominated portion of the Roadside prescribed burn

had slightly more severe fire effects due to higher fuel loadings (some fuel

loadings were 80 tons/acre). As a result, bark char heights averaged 7 m on

the front side and 3 m on the backside. Scorch heights were higher averaging

76% of live crowns scorched. Prior to prescribed burning, many of the ponderosa pines had the needle cast, Elytroderma dispars, which may have contributed to higher live crown scorch measures.

At Spooner Summit, 26 trees with complete crown scorch had significant

green bud break one year after the fall fire season of 1996. Only one tree was

blatantly killed by fire with 100% crown scorch and complete bark char (Code

3) down to the cambium. Bark char heights averaged 7 m on the front side

and 3 m on the backside. Live crown scorch heights were a bit higher than the

Roadside spring burn averaging 60% of live crown scorched. Using a simple

paired T-test to compare fire severity measures at both sites, the difference in

bark char codes for the back of the tree and the percentage live crown were

significant with a 95% confidence interval. All three front bark char codes

were not significantly different in these two populations. Different weather

conditions and ignition patterns alone may have caused these differences in

back bark char codes and percentage live crown scorched.

Insect Activity Monitoring

After the Spooner Summit fall burn, 31% of the Jeffrey pines were hit by Ips

pini and D. valens in the first year. D. valens, with a range of 1-25 attacks per

tree, hit 53% of the Jeffrey pines (125 trees). The trees hit by D. valens averaged 30.2 cm in dbh (SD of 16) and six D. valens attacks per tree (SD of 5)

Table 3—Summary of fire monitoring for Lake Tahoe Basin Management Unit’s Spooner

Summit Fall Burn and Lassen Volcanic National Park’s Roadside Unit Spring Burn. Prefire data collected in 1996 for Spooner and 1997 for Roadside.

Spooner Summit LNP Roadside Burn

Season of prescribed burn Fall Spring

Pre-burn fuel loading 5.13 tons/acre (SD = 6.8) 7.9 tons/acre (SD = 6.5)

Post-burn fuel loading 4.87 tons/acre (SD = 5.3) 4.74 tons/acre (SD = 5.5)

Fine fuels consumed 5.48 cm (SD = 2.3) 3.95 cm (SD = 2.9)

Bark char front code 1 4.25 m (SD = 3.7) 3.77 m (SD = 3.9)

Bark char front code 2 1.77 m (SD = 1.9) 1.86 m (SD = 2.1)

Bark char front code 3 0.55 m (SD = 0.6) 0.70 m (SD = 0.8)

% live crown scorch 60 56

Ganz, Dahlsten, and Shea The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments
USDA Forest Service Proceedings RMRS-P-29. 2003. 151

while those plots outside the burn only had one incident of a tree attacked by

D. valens. Fifty-nine percent of the burned trees attacked by D. valens (73

trees) were also hit by Ips pini. Only 12 trees were hit by Ips pini without the

presence of D. valens. These 12 trees were hit by Ips pini without the presence

of D. valens (representing only 13% of the Ips pini activity). This might indicate some type of predisposal or chemical attractants with D. valens.

In this Lassen Roadside Unit spring burn, six trees of the total sample (87)

were initially killed by the fire. In year one, 13 were outright killed by Ips pini

(15%), one by D. jeffreyii and 11 by S. ventralis. Twenty-three trees were hit

by D. valens, averaging 16 attacks per tree. By 2001, 50 trees had been killed

by one of these four insects. Eight trees within the white fir-dominated area

had S. ventralis and T. abietis, all of which shared extremely high severity

measures with crown scorch of 0-90% and bark char heights shown of 5 m

front side and 1.75 m backside for Code 1 and 1.6 m front side and 0.73 m

backside for Code 3.

Sampling for the first season shows 35% of pines hit with D. valens and 24%

with Ips pini by late-August. Each tree hit by D. valens more than 20 times

was also subsequently hit by Ips pini in this first sampling season. Trees hit by

D. valens averaged 20 attacks per tree, ranging from 2 to 131 pitch tubes.

Although not the intent of this study to document the timing and duration of

insect activity, field observations noted two distinct time periods when newly

injured trees are susceptible to D. valens attack: first early in the summer and

then again at the end of the summer months.

Trees at the Lassen Volcanic National Park’s Roadside spring burn had steady

increases in the number of D. valens attacks throughout the first season (figure 2). Here, initial tree attack took place on June 23 (24 hours after the

The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments Ganz, Dahlsten, and Shea

Figure 2—Red turpentine beetle activity during the summer months of 1999 after spring prescribed burning

within Roadside Unit, Lassen Volcanic National Park. Initial tree attack, denoted by series, took place on June 23

(24 hours after the burn) and subsequently no new trees were infested until the last sampling on August.

D. valens Trends Through Summer of 1999

0

20

40

60

80

100

120

140

Dates of Visits to LNP

Series1

Series2

Series3

Series4

Series5

Series6

Series7

Series8

Series9

Series10

Series11

Series12

Series13

Series14

Series15

Series16

Series17

Series18
152 USDA Forest Service Proceedings RMRS-P-29. 2003.

burn) and subsequently no new trees were infested until the last sampling

period on August 25 (figure 2). At Blodgett Research Forest in El Dorado

County, Owen (1985) found that the largest pulse of D. valens took place in

the first three weeks of May when daily temperatures had increased. The Roadside spring burn is at higher elevations with a range of 1725-1798 m (5660-

5900 ft) compared with that of Blodgett Forest at 1200-1550 m (3900-4800

ft) and therefore the D. valens pulse may be delayed due to cooler temperatures. Other environmental factors may be playing a role in determining when

such a D. valens is likely to occur in a given year. As this is a poorly studied

insect in California, more work on the flight periodicity of D. valens is needed.

For a comparison of burning effects on insect-induced tree mortality, a

paired T-test was used to evaluate the populations of burned and non-burned

trees at Spooner Summit and Lassen Volcanic National Park (comparing 323

burned trees with 112 unburned trees outside of the two prescribed burns).

The null hypothesis for this test is there is no difference between means of

those trees killed by insects (KBI) between burned and unburned populations. The test rejected the null with 95% confidence and a T-value of 8.58

with 433 degrees of freedom and a P of 0. There is a significant difference

between the two population means, which in this case, is the proportion of 1’s

since KBI is binary (0 = alive, 1 = dead). Since it is the proportion or rates of

mortality that is of interest to this study, this is a suitable test to run. Ordinarily with such binary data, a nonparametric test that can be applied to two

independent sets of sample data would be used (like a Z-test). This Z-test was

thus applied to this data. The Z-distribution is a standard normal distribution

and the Z-test statistic is calculated by Z=(X-mean of X)/(standard deviation

of X), i.e., number of standard deviations away from the mean for any normal

data, X. Using this test, the Z-value for KBI is significant with Z = 8.5033 and

P = 0.

For a comparison of seasonal effects on insect-induced tree mortality, a

paired T-test was used to evaluate the populations of those trees killed by

insects in both fall and spring populations of Spooner Summit and Roadside

prescribed burns. The null hypothesis for this test is there is no difference

between means of those trees killed by insects (KBI) between fall and spring

populations. The test accepted the null with 95% confidence and a T-value of

-1.6384 with 321 degrees of freedom and P = 0.1023. There is a no significant difference between the two population means, which in this case, are the

percentages of trees killed per seasonal window.

Discussion

At the Roadside Unit, the white fir component experienced considerable

mortality in the smaller size classes (5-10 cm) while the ponderosa pine component experienced mortality in the larger size classes (five size classes from

20-70 cm). Bark beetles are contributing to most of this pine mortality with

only six trees killed by the prescribed fire outright. Given the remaining live

overstory component (figure 3), it is probable that white fir will retain dominance over the growing space of the site. It will probably take two to three

more fires of this intensity to restore the pre-fire species composition as indicated by the pine snags in the area. In the pine-dominated area of the burn,

the higher mortality rate is most likely attributed to a high water table. Old

Park Service maps show this area as a grassy opening or a high elevation meadow.

Under a more frequent fire regime, this area is likely to revert back to a high

Ganz, Dahlsten, and Shea The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments
USDA Forest Service Proceedings RMRS-P-29. 2003. 153

elevation meadow. With fire suppression, pines have established themselves

but not with high vigor. This assessment was made by looking at live crown

ratios which are remarkably low for this aspect exposure. A general rule of

thumb is that trees living in an open, relative flat environment, such as the one

at Roadside, would be expected to have at least 30% of its height in vigorous

green growth2

. However, the trees inspected had only 20% of their total height

in a yellowish crown that was riddled with Elytroderma dispars. It is speculated that these trees were going to die with or without prescribed burning

but at a slower time scale. Therefore, the increased mortality rates from this

spring burn can be potentially seen as speeding up succession back towards a

meadow landscape.

Historically, D. valens, otherwise known as the red turpentine beetle, has

not been considered a primary mortality agent in ponderosa pine and Jeffrey

pine (Jenkinson 1990). As a lower bole specialist, this insect has been prolific

in cutover stumps and predisposed trees but never of much concern to forest

managers. Only under rare conditions was it seen overwhelming trees (Smith

1961), usually with exotic introductions and/or other human interventions

such as overwatering, fertilizing, soil displacement around root systems, etc.

Fire-scorched trees frequently have had sizable populations of this beetle (Eaton

and Lara 1967). Mitchell and Martin (1980) speculated that a program of

systematic prescribed burning could stimulate a population of D. valens to

levels where it could overwhelm trees or predispose trees to other mortality

agents. Owen (1985) observed a spring prescribed burn at Blodgett Forest,

El Dorado County, where 80-90 scorched trees were heavily attacked by D.

valens. On a wildfire 16 km east of Blodgett Forest in the Rubicon River

drainage, Owen again observed unusually high attack rates with 80% of Pinus

ponderosa and Pinus lambertiana attacked (Owen 1985). Ferrell (1996) goes

The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments Ganz, Dahlsten, and Shea

100

80

60

40

20

0

00 PRE n=1

Trees/hectare (s.e.)

01 yr01 n=1 01 yr02 n=1

Abies concolor 10.0-19.9 cm Abies concolor 20.0-29.9 cm Abies concolor 30.0-39.9 cm Abies concolor 40.0-49.9 cm

Abies concolor 50.0-59.9 cm Abies concolor 60.0-69.9 cm Pinus ponderosa 10.0-19.9 cm Pinus ponderosa 20.0-29.9 cm

Pinus ponderosa 30.0-39.9 cm Pinus ponderosa 40.0-49.9 cm Pinus ponderosa 50.0-59.9 cm Pinus ponderosa 60.0-69.9 cm

Figure 3—Live overstory tree density by species and size classes within the Lassen Park Roadside spring

prescribed burn, Lassen Volcanic National Park, Lassen County, CA. Pre-burn data sampled in 1997,

post-burn in 1999-2000.

2 Monitoring Tree Health on Private

Woodlands, University of Idaho Extension Website http:/www.ets.uidaho..edu/

extforest/.
154 USDA Forest Service Proceedings RMRS-P-29. 2003.

further in identifying the probable increase in D. valens in the Sierras following the increased use of controlled burning.

In the case of the Spooner Summit fall burn, Lake Tahoe Basin Management Unit, 31% of the Jeffrey pines were attacked by Ips pini and D. valens.

These results are comparable with another study in the Tahoe Basin which

found 23% of its sampled trees attacked within the first two sampling seasons

(Bradley and Tueller 2001). In another study on the Harvey Mountain spring

burn, Lassen County, large open grown trees with high fire severity measures

had large lower branches 15.2 cm (6 in) in diameter infested with pronounced

Ips pini and D. valens activity on the lower bole (Ganz 2002). It could be that

fire-injured small diameter material is creating a reservoir for both of these

species but especially for Ips pini, which usually attacks slash piles and tops of

trees (Furniss and Carolin 1977).

High variability in fire effects to trees was observed throughout the two

prescribed burning study areas, consistent with the range of thermal effects

mentioned by Schmidt (1996). D. valens and Ips pini activity at both sites was

particularly high, perhaps due to the high intensity of burns and a stressful

environment. Spooner Summit has a southwestern exposure, shallow soils,

steep slope, and heavy winds. Beetle-induced mortality was already evident as

early as July 9 when plot layout was performed. As previously mentioned, a

high water table is speculated to play a role in the Roadside pine-dominated

area. Regardless of these two site’s predisposition to bark beetle attack, there

needs to be a re-evaluation of traditional perceptions of Ips pini and D. valens

and their ecological niches in forested ecosystems following the disturbance

of fire. Ips pini and D. valens are contributing to high levels of pine mortality.

Research from Blacks Mountain Experimental Forest (BMEF) has used the

same fire severity measures and has found that the bark char measures are

useful for deciphering between those trees killed by fire and those killed by

insects (Ganz 2002). In this study, the combination of percent live crown and

bark char codes greatly increased the ability of models to predict tree mortality, both from the fire itself and from subsequent insect attack. In all models,

these two fire severity measures had a positive relationship with increasing

probability of tree death. In general, the models for KBI performed better

with higher predictability using the fire severity measures than those models

for killed by fire (KBF). Also noteworthy from this study was the fact that

those models for KBI performed best with the use of Bark Code 2 (front and

back) while those models predicting KBF performed better with Bark Code 3.

Biologically, this inherently makes sense as insects are less likely to invade

material that has no nutritional value (which Code 3 probably indicates). Also

relevant to these two cases at Lassen Volcanic National Park and Spooner

Summit was the finding that the same fire severity measures performed differently from tree species to tree species and between individual insects within

one tree species (Ganz 2002). For instance, the Ips pini response model on

the BMEF data set performed differently than the Dendroctonus ponderosae

response model with regard to the emphasis of particular fire severity measures. Given that Lassen Volcanic National Park and Spooner Summit have

very distinct forest composition, the insect responses may differ even more

than the BMEF results. Differences in years and seasonal windows (although

not found in this particular case comparison) are likely to further convolute

these results. Interactions with insect parasites, insect predators, and avian

predator complexes, which are known to affect bark beetle populations

(Berryman et al. 1970), will vary from spring to fall and from year to year.

Further work is recommended to determine the role of these interactions,

specifically with different pine species and their post-burning mortality rates.

Ganz, Dahlsten, and Shea The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments
USDA Forest Service Proceedings RMRS-P-29. 2003. 155

The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments Ganz, Dahlsten, and Shea

Conclusion

A primary goal of restoration treatments in ponderosa pine forests is to

create more open-stand structures, thereby improving tree vigor and reducing vulnerability to insects, disease, and severe fire. Alternatively, the use of

prescribed fire has increased the concerns about the detrimental impacts of

fire on tree vigor, especially for its potential to predispose large trees to bark

beetle attacks. In the same manner that a management tool has the potential

to increase tree vigor and site productivity (site quality), it also has the potential to reduce it through misapplication and site degradation. Prescribed fires

have been shown to have such a potential. Growth losses on commercial tree

species have been reported in response to fire stress. These losses are usually

attributed to root, cambial, and crown damage, leading to a decline in the

physiological condition or vigor of the tree (Hare 1961). Other potential

sources of losses may be attributed to fuel consumption of organic material

and topsoil loss through the process of erosion (Agee 1973). This study has

documented some of the effects of cambial and crown damage from two different prescribed burning treatments.

Insect-induced mortality following prescribed burning may be further assessed with specific knowledge of the insect’s ecology and the post-burning

response. Such knowledge intrinsically can add to the value of prescribed burning as a tool for creating heterogeneity in post-burning tree survival and subsequent recruitment. Although not found in this particular two case

comparison, insect-induced mortality differences are likely to occur by seasonal windows and by year (especially under drought conditions). The use of

prescribed burning with this embedded knowledge of insect-induced mortality will allow managers to change stand densities, size distribution, and community composition. For instance, many low intensity fires have little effect

on the relative density of the stand because the average tree size will change

little although the density will decrease through the loss of small stems both

from the fire itself and the presence of bark beetles specializing in small diameter classes. Alternatively, the opposite may take place with delayed mortality

in the larger diameter classes (due to opportunistic bark beetles) having a

pronounced impact on relative densities. An increased comprehension of these

fire-insect interactions is necessary for more effective science-based management of forest ecosystems (McCullogh et al. 1998). Given these two cases

from Lassen Volcanic National Park and the Lake Tahoe Basin, tree mortality

from bark beetles can reduce stocking levels of pine trees which, given the

management objectives of the prescribed burn, may be desirable or undesirable. Many have claimed the use of prescribed burning as a means to restore

the competitive advantage to the pine species by thinning out shade-tolerant,

understory white fir trees. This would create the growing space for pine regeneration or allow remnant large pines to retain dominance and vigor with

less water stress resulting from the thinning of neighboring competition. But

the two study cases indicate otherwise, further demonstrating the need to

consider the management implications of post-burning bark beetle responses.

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McCullogh, Deborah.G.; Werner, R. A.; Neuman, D. 1998. Fire and insects in

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Entomology. 43: 107–127.
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The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments Ganz, Dahlsten, and Shea

McKelvey, Kevin.S.; Skinner, C. S; Chang, C.; Erman, D. C.; Husari, S. J.; Parsons,

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Oliver, William W. 2000. Ecological research at the Blacks Mountain Experimental

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Ganz, Dahlsten, and Shea The Post-Burning Response of Bark Beetles to Prescribed Burning Treatments
USDA Forest Service Proceedings RMRS-P-29. 2003. 159

Root Pathogens and Fire: Silvicultural

Interactions in “Exotic” Ecosystems

William J. Otrosina1, Susana S. Sung1, Charles H. Walkinshaw1, and

Brian T. Sullivan1

Much attention is now given to risks and impacts of exotic pest introductions in forest ecosystems. This concern is for good reason because, once introduced, an exotic pathogen or insect encounters little resistance in the native plant population and can produce catastrophic losses in

relatively short periods of time. Most native fungal pathogens of forest trees

have co-evolved for eons with their hosts and have reached a sort of balance

between them and populations of susceptible tree species. Recent studies on

various forest types have indicated a higher incidence of certain fungal pathogens than were previously thought to occur. These pathogens are either the

type not normally thought of as highly virulent or are those that have not

been previously reported as a serious problem on a particular host. For example, pathogenic fungi belonging to both the Leptographium /Ophiostoma

complex and Heterobasidion annosum are associated with mortality after prescribed burning in certain longleaf pine stands. Yet, this tree species has traditionally been ranked as highly tolerant to these fungi. In some Sequoia

giganteum sites, fire suppression has led to encroachment by true firs. The firs

developing in the understory have been shown to harbor H. annosum which

infects the Sequoia via root contacts. Could these observations reflect some

manifestation of “exotic ecosystems,” whereby the conditions under which

particular tree species evolved are no longer present or are altered in some way

that increases their susceptibility to these fungi? With the current emphasis on

ecosystem restoration and alternative silvicultural regimes, it is critical to

address questions dealing with impacts resulting from implementation of ecosystem restoration and other management objectives order to avert losses in

forest productivity.

1USDA Forest Service, Institute for TreeRoot Biology, Athens, GA.
160 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 161

Treatment—Economics
162 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 163

Social Sciences and the Economics of

Moderation in Fuels Treatment

Douglas B. Rideout1

Abstract—Fuels management is conducted in the context of the social sciences,

which bring the science of the human element into the analysis. Of the social sciences, economics addresses the enhancement or improvement in the human condition

by improving our ability to allocate scarce resources. The current fuels treatment

situation suggests very ambitious ends in the context of limited means. Scarce resources mean that we will not have all of the fuels treatments that we would like.

Therefore we will have to choose wisely among alternative treatment options. Socially desirable treatment programs will recognize both the costs and benefits of

treatments to seek desired treatment levels. Further, social science investigations will

reveal unintended side effects of fuels treatment policies. The challenge upon us is

to combine the information on treatment standards, ecology, and technology into

line with the social sciences to attain socially desirable treatment systems.

This page marks a transition in the conference proceedings from the

physical and ecological perspectives to considerations of the social sciences. Also, the following papers primarily address the results of specific case

studies. This shift in focus is fundamental, so a few contextual remarks regarding the social sciences and their role in fuels treatment are appropriate.

Social sciences use the scientific method to address laws and hypotheses of

human values and behavior. Of the social sciences, economics addresses the

connection between resource allocation and social welfare. By social welfare,

economists mean improving the human condition, or making people better

off. We benefit in many ways from fuels treatments, but they are also costly. In

a world of scarce resources and limited budgets, difficult choices are required.

Resource allocation has long been an important consideration in fire management. As stated by fire historian Steven Pyne et al. (1996):

“Economic theory has long enjoyed special privilege as a mechanism of

reconciling fire management’s limited means with its ambitious ends.”

Papers in these proceedings, recent studies by the U.S. Government

Accounting Office, and the President’s National Fire Plan are among many

indicators that the ends may never have been so ambitious relative to the

means. The potential for substantive contributions from the social sciences

may also have never been greater.

Fuels management for purposes of ecosystem preservation, maintenance,

restoration, and wildfire suppression are conducted in a context of scarce resources. Scarcity means that we cannot have all of the things that we would

like. This pervasive condition applies as well to ecosystem restoration promoted by fuels treatments. In a world of scarce resources, choices are imperative

and many choices can be difficult and frustrating.

For federal programs, budget limitations are a practical reflection of scarcity. Each of the fuels management activities noted above is subject to limited

budgets that require difficult choices. These choices all require the careful consideration of the treatment benefits versus treatment costs. Such consideration

suggests a principle of moderation. The principle is simply stated as: “When

1Professor, Forest Economics, Colorado

State University, Fort Collins, CO.
164 USDA Forest Service Proceedings RMRS-P-29. 2003.

there are both benefits and costs of an endeavor, there is typically a socially

optimal level of the activity.” Extreme solutions are impractical and wasteful.

For example, social scientists have long known that a pristine environment is

economically and socially undesirable. It is too expensive. It would detract

from the achievement of too many other things that society values thereby reducing overall social welfare. As a society, we have chosen to accept a less

than pristine environment in exchange for other goods, services and opportunities. The hard question and the relevant question is “What is the level of

environmental quality that will best benefit society?” Ecologists, engineers,

and economists have been hard at work on this problem for years.

Adoption of the 10 AM policy in 1936 cast aside the principle of moderation expressed in the 1916 and 1925 works of Headly and Sparhawk. The

works of Headly and Sparhawk were the first expressions of the tradeoff between fire management costs and fire damage. These works sought a balance

between the two costs that would minimize total cost. The 10 AM policy stipulated fire control by 10 AM the morning following the report of a fire. It was an

extreme response to extreme conditions. As such, it disregarded costs, lacked

moderation, and was therefore socially undesirable. Pyne et al. (1996) compared

the policy to panic legislation promulgated by savage droughts in the early 1930s.

Concerned that costs were spiraling out of control, the early economic works

of Headly and Sparhawk were summoned to advance a more cost-responsive

policy. The Forest Service replaced the 10 AM policy in 1978 by what became

known as “cost plus net value change” or C+NVC. Like the early works,

C+NVC embodies the tradeoff between the costs of wildfire management and its

benefits (Rideout and Hesseln 2001). It therefore directly embodies the notion of

scarcity and seeks to promote an optimal extent of wildfire management and resource damage. This shift from an extreme and reactionary policy to a thoughtful recognition of tradeoffs marked a fundamental advancement in wildfire policy.

The fully restored ecosystem over a broad scale is potentially analogous to

the 10 AM fire suppression policy. Such restoration would seem to preclude

the notion that treatments, including fuels treatments, are conducted in the

context of scarcity. While full ecosystem restoration may be desirable, in a

world of scarce resources we are ultimately forced to focus on optimal restoration. If, for example, funds are available to only treat 5 or 10 percent of the

problem, as many federal land managers have suggested, then how are those

funds best allocated? Which lands would be best to restore and to what level?

How to design optimal treatments for different ecosystems and social conditions will address many of the key questions of this era.

Ecosystem restoration through fuels treatment can be viewed as a project in

environmental quality. The basic economic model of restoration is shown in

figure 1 and is identical to the correctly formulated theory of C+NVC. When

the benefits of restoration are considered in context with the costs, the principle of moderation is illuminated. The principle suggests an optimal level of restoration consistent with R* in figure 1. Here, the marginal cost of restoration equals its marginal benefits. The extreme response of full restoration on a

broad scale would be at the right-hand edge of the figure where costs are

excessive relative to benefits.

Figure 2 illustrates the tradeoff between the costs and benefits of restoration in a budgeting context. The optimal restoration budget would correspond

with the budget provided by the area under the MC curve up to point R*.

(Ignoring fixed costs for the moment does not alter the overall point.) A presumption that restoration is limited by the budget is perhaps correct, but it

may ignore the social issue of moderation. Anecdotal evidence often suggests

that budgets are consistent with the level indicated by B

1. They are too low

Rideout Social Sciences and the Economics of Moderation in Fuels Treatment
USDA Forest Service Proceedings RMRS-P-29. 2003. 165

relative to benefits. Unfortunately, the dearth of social research has made it

difficult to confirm this suggestion. Likewise, the excessive budget resulting

in level B2 has not been soundly rejected with the current level of research in

the social sciences.

The principle of moderation embodied in figures 1 and 2 would suggest

that a different set of questions need to be addressed if we are to advance the

fuels treatment paradigm beyond the equivalent of the 10 AM policy. Many of

these questions address what economists would refer to as the intensive and

extensive margins of fuel treatments.

The intensive margin suggests questions regarding how fully ecosystems

are restored. For example:

• Is full restoration always necessary or desirable?

• What is the design criteria for less intensive treatments?

• What are the beneficial and detrimental ecological effects of partial treatment or restoration?

Questions regarding the extensive margin would include:

• Which ecosystems are best suited for full and partial restoration?

• In a given ecosystem and/or ecosystem condition, how many acres should

be treated?

Ecological studies that have focused on extremely intensive treatments from

small case-study sites provide valuable information and effectively benchmark

the full-treatment option. However, ecological research could add much to

the field by further addressing the implications of managing the intensive and

extensive margins. While the questions above provide a sample of the relevant

questions suggested by social considerations, clearly they are interrelated. For

example with limited budgets, treating fewer acres means that acres that are

treated can be treated more intensively.

The literature and research on fuels treatment has been largely biocentric.

For example, in their chapter on fuels treatment and in their section on fuel

management, Pyne et al. (1996) contains no discussion of the social sciences.

(This is provided as evidence of the lack of research in the field and not as a

comment on the book or the authors.) With a biocentric history in fuels

research, it should come as no surprise that fuels management and policy

searches for cohesive social paradigm.

R*

MC

MB

$

Ecosystem Restoration

R*

MC

MB

$

B1 R* B2

MC

MB

$

B1 B2 Ecosystem Restoration

Figure 1—Optimal ecosystem

restoration.

Figure 2—Optimal ecosystem

restoration and a budget.

Social Sciences and the Economics of Moderation in Fuels Treatment Rideout
166 USDA Forest Service Proceedings RMRS-P-29. 2003.

Non-market benefits, including those associated with ecosystem restoration, can be problematic to assess on a broad scale. In effect, the location and

shape of the marginal benefit curve in figure 1 may be impractical and too

costly to accurately estimate. While this does not detract from the philosophical strength of the model, it suggests that measures of treatment effectiveness,

such as acres changing condition class, may need to be introduced as surrogates for benefit data. This is another area where research in the social sciences

has the potential to enhance the fuels treatment paradigm.

Social scientists are trained to reveal unintended side effects of new programs and initiatives. For example, federal standards required air-bags in

automobiles with the intent of improving highway safety, but early data showed

that fatality rates were unresponsive. Why? Because drivers increased their speed

and took more risk knowing that their level of protection had improved.

Similarly, extensive fuels treatments in the wildland urban interface will have

unintended social side effects. We are just learning about the unintended side

effects of intensive treatments in the interface. For example, as we lower the

risks of living in the interface, its desirability will increase. More homes will be

built and people will build more expensive homes. The value of property to protect may increase partly as a result of treatment efforts. Just as there are still fatalities

on the highway, there will still be homes that burn in the interface. This is the

principle of moderation of treatment at work—like a pristine environment, a failsafe highway is simply too expensive and so too is a fail-safe interface.

So this morning I offer a challenge. Can we combine information on treatment standards and technology with the social sciences to manage the problem

in a socially desirable way—in a way that recognizes that we live in a world of

scarce resources with diverse public opinions and dynamic social values? Only

by integrating the ecology of treatments with the social sciences can we properly address the problems of fuels treatments in ways that will advance societal

welfare. The papers that follow on the social sciences will explain the results of

studies on cost analysis, social values of fire, recreation, big game, collaboration, community involvement, optimal treatment location, and more.

Literature Cited

Headley, R. 1916. Fire Suppression District 5. USDA Forest Service. May 1, 1916. 58p.

Pyne, S.J.; Andrews, P.L.; Laven, R.D. 1996. Introduction to wildland fire. 2nd ed.

New York: John Wiley and Sons, Inc. 769 p.

Rideout, D.B.; Hesseln, H. 2001. Principals of forest and environmental economics,

2nd ed. Fort Collins, CO: Resource and Environmental Management, LLC. 327 p.

Sparhawk, W.R. 1925. The use of liability rating in planning forest fire protection.

Journal of Agricultural Research. 30: 693-792.

Acknowledgments

This paper has benefited from comments and suggestions provided by John

Hof, Susan Howell, and Ryan Buller. They are, respectively, Chief Economist

at the Rocky Mountain Research Station (USDA Forest Service), Graduate

Student in Forest Economics in the Department of Forest Sciences at

Colorado State University, and Undergraduate in the Department of Forest

Sciences. Thanks to Phil Omi and Linda Joyce for asking me to lead this session of

the fuels conference and for their valuable suggestions. The usual disclaimer

applies.

Rideout Social Sciences and the Economics of Moderation in Fuels Treatment
USDA Forest Service Proceedings RMRS-P-29. 2003. 167

Costs for Reducing Fuels in Colorado Forest

Restoration Projects

Dennis L. Lynch1 and Kurt Mackes1

Abstract—The costs to either mechanically remove or mechanically treat forest fuels

are examined for various Colorado sites. In the ponderosa pine and mixed conifer

zones, no ideal treatment system exists yet for forest restoration through fuel reduction.

Each site requires its own ecological analysis. Costs for forest restoration varies by

ecological prescription, forest and terrain conditions, and market availability for

potential products. In most cases, it may cost too much to remove material or treat

areas. Logging may be the most economical method if markets for products exist and

processing facilities are nearby.

Introduction

What does it cost to reduce fuels in forest restoration projects in

Colorado? Our research discusses such costs at several locations across

the state. A key objective of forest restoration is the reduction of the number

of trees and other fuels on the site. An ecological prescription, written by a

professional other than the authors, was used to direct each project studied

and was specific to the forest type and fuel conditions on the site. In most

cases, fuel materials in the form of logs, posts, poles, pulpwood, and chips

were removed from the site to reduce fuel loading and achieve desired restorative effects. In a few cases, fuel arrangement was more of a problem than fuel

loading and vegetation was mechanically processed and left on the site. A

variety of equipment was used for these mechanical treatments. Equipment

varied by owner-operator and the type of treatment required.

This paper focuses on costs to mechanically remove fuels from each site or

to mechanically treat fuels on a site. It does not include costs associated with

wildlife habitat or watershed improvement, for example, which may be a part

of a forest restoration program. It does not, therefore, attempt to distribute

costs to multiple benefits associated with forest restoration or fuels reduction,

a problem reported on by others, such as Gonzalez-Caban and McKetta (1986).

In most cases, mechanical removals for forest restoration are followed by the

use of prescribed fire to reduce fuel loading on the site. This paper does not

report on costs associated with the use of prescribed fire. The following is a

summary of project studies we have completed through 2001.

Projects Where Fuels Were Removed From

the Site

In the following projects, trees were cut and products were removed from

the site to reduce fuel loading and achieve restoration objectives. The types of

products varied depending on the markets available at that time.

1Department of Forest, Rangeland and

Watershed Stewardship, Colorado State

University, Fort Collins, CO.
168 USDA Forest Service Proceedings RMRS-P-29. 2003.

 Ponderosa Pine Partnership Project

This study has been extensively reported in several publications (Preston

and Garrison 1999, Richard and Burns 1999, Lynch, Romme and Floyd 2000,

Lynch 2001). It was the first forest restoration project in Colorado, located

on the Dolores District of the San Juan National Forest near Cortez, Colorado.

Since it was an experimental project, it was sold as an administrative research

timber sale to Montezuma County who subcontracted removal of trees to

local loggers. The County was concerned that the area was at substantial risk

to insect attack and catastrophic fire that could threaten lives, property, and

livelihood of county residents. Major data collection efforts occurred during

1996 to 1998 although project development preceded and monitoring

followed those dates. Dr. William Romme, who also conducted follow-up

ecological monitoring, wrote the ecological prescription for this ponderosa

pine forest (Lynch, Romme, and Floyd 2000). This prescription called for

reducing the number of trees per acre. Prior to the project there were 280 to

390 trees per acre versus 40 to 50 trees per acre in the 1900 pre-European

settlement forest (Romme, Grissino-Mayer, Floyd, Hanna 1999). The key

objective of this project was the reduction of trees per acre toward the lower

1900 density to reduce fuel loading and improve fuel arrangement. The largest trees on each site were retained. Those trees were used to identify clumps

that varied from a group of six trees up to as much as one-third acre in size.

Openings of varying sizes were created between clumps. Snags were retained

for wildlife. Phil Kemp, USDA Forest Service, designed silvicutural techniques

and marking guides to carry out Dr. Romme’s prescription. Dr. Dennis Lynch

conducted economic data collection and financial analysis. Mechanical removal

of trees was accomplished by Ragland and Sons Logging of Dolores, Colorado.

Trees were cut using chainsaws and a JD 743 harvester. Logs were skidded

using either a JD 540 or a CAT 518 rubber tired skidder. Logs were loaded

on conventional log trucks using a knuckle boom loader for transportation

from the forest to several mills producing a variety of finished products.

Lynch (2001) reported very detailed cost information associated with

mechanical removal for the 492.6 acres treated in this project. Costs included

equipment use, fuel, fluids, workers’ wages, administration, insurance, bookkeeping, and fees paid by the logger. All trees that were cut and removed were

counted by diameter class. All products removed were weighed and all

revenues paid to the logger by mills and other buyers were recorded. Table 1

summarizes the percent of material removed by diameter class in each unit,

tons removed per acre, number of acres per unit, and treatment cost per acre

for each unit.

The average per acre treatment cost for all units in this project was $869.24.

Products such as sawlogs, waferwood logs, posts, poles, and pulpwood were

removed from this area and revenues were generated from their sale. The

Table 1--Ponderosa pine project removals, acres, and cost per unit. 

Unit Acres Species 3" to 4.9" 5" to 7.9" 8" to 11.9" 12" + Tons/acre Cost/acre

Unit 1 125 Ponderosa No data 50.1% 39.2% 10.7% 29.6 893.22 $ 

Unit 4 95 Ponderosa No data 48.4% 41.1% 10.5% 33.3 1,173.35 $ 

Unit 5B 108 Ponderosa No data 40.9% 51.8% 7.3% 35.0 985.96 $ 

Unit 5E 65 Ponderosa No data 44.3% 51.4% 4.2% 23.5 794.77 $ 

Joyce 100 Ponderosa No data 22.0% 26.0% 52.0% 21.7 497.26 $ 

Percent removal by diameter class

Lynch and Mackes Costs for Reducing Fuels in Colorado Forest Restoration Projects
USDA Forest Service Proceedings RMRS-P-29. 2003. 169

average revenue from such sales was $882.35 per acre. Therefore, the logger

who conducted the work had an average profit of $13.11 per acre with a total

for the entire sale of $3,852.36 before taxes. This was less than 1% profit

(0.8%) before taxes on revenues. However, the USDA Forest Service received

stumpage payments (a part of the costs reported above) for the material

removed that resulted in a return of $79.08 per acre. These payments included fees for the trees removed, rock replacement for the road, and a collection for slash disposal which was used to assist with prescribed burning.

Mixed Conifer Project

This project followed the Ponderosa Pine Project but has received relatively

little publicity. Lynch and Jones (1998) published detailed information on the

ecological prescription, costs, and revenues in their summary report. Dr. Wayne

Shepperd, Rocky Mountain Research Station, and Bruce Short, current USDA

Forest Service Region 2 Silviculturalist, designed the ecological prescription

for this mixed conifer forest. The project was located in the Gordon Creek

drainage of the Pagosa Springs District of the San Juan National Forest. The

untreated forest consisted of an overstory of large ponderosa pine and Douglasfir with a dense understory of small white fir and scattered clumps of aspen.

The prescription called for retaining large overstory trees and removing all

white fir within 100 feet of any ponderosa pine or Douglas-fir overstory tree

as well as cutting aspen clumps to induce sprouting. Rue Logging, Inc. of

South Fork, Colorado, did the mechanical treatment during October and

November of 1998. Chainsaws were used to cut trees and logs were skidded

with a JD 648 and two JD 650 skidders. A JD 550 skidder was used to clean

the large volume of slash created from the white fir trees. A Prentice 410C

knuckle boom loader loaded logs on conventional log trucks for transportation to local mills. All trees cut were counted by diameter classes (table 2). All

harvesting costs, including administration and fees paid, were collected in

cooperation with the logger and converted to an average per acre cost (table

2). The white fir material that was salvageable was taken to the stud mill at

South Fork, Colorado where the logger was paid on a per ton basis. The aspen

logs were taken either to the excelsior plant in Mancos or the paneling plant

near Dolores, Colorado, where revenues were paid on a per ton basis.

In this project, revenue from white fir logs for studs, aspen logs for paneling, and aspen logs for excelsior were used to offset costs. Revenues realized

from the sale of white fir and aspen products were converted to an average per

acre revenue of $1,272.16 resulting in a profit to the logger of $62.97 per

acre before taxes. This was a 4.95% profit before taxes on revenue. The USDA

Forest Service charged stumpage fees for the material removed that resulted

in a return to the agency of $217.86 per acre. These fees included charges for

the trees removed, rock replacement for the road, and a collection for slash

disposal, which was used for prescribed burning.

Table 2--Mixed conifer removals, acres, and costs.

Unit Acres Species 3" to 4.9" 5" to 7.9" 8" to 11.9" 12" + Tons/acre Cost/acre

Gordon White fir 49.1% 15.1% 17.5% 18.3% 36.1

Creek Aspen -- 30.2% 69.8% -- 10.7 75 $1,209.19

Percent removal by diameter class

Costs for Reducing Fuels in Colorado Forest Restoration Projects Lynch and Mackes
170 USDA Forest Service Proceedings RMRS-P-29. 2003.

Fox Run Regional Park Project

The Fox Run Project was the first attempt at forest restoration on the Front

Range of Colorado. This project was located in the Black Forest near Colorado

Springs. It was a cooperative venture between El Paso County Parks and the

Woodland Park District of the Colorado State Forest Service. To return the

Park’s forest to a more natural condition, forest restoration research from the

southwest was modified by Chuck Kosteka and Dave Root of the Colorado

State Forest Service into an ecological prescription that also met Park objectives. Specific management objectives were:

1. Restore the health and vigor of the forest by selective thinning of trees.

2. Reduce the severe fire hazard and improve insect and disease resistance.

3. Enhance the esthetics and recreational enjoyment of the park.

4. Reduce the uniformity of the stand to enhance the number and variety of

wildlife species.

5. Provide a demonstration area with interpretive signs.

6. Retain enough trees around the picnic area to provide screening

between the tables.

The ponderosa pine forest contained 548 trees per acre with a basal area of

128 square feet and a spacing of approximately 6 feet between trees. The

average dbh of the stand was 6.5 inches and the average height was 42 feet.

Fire hazard was rated as severe. The prescription called for reducing the basal

area of the stand to 80 square feet per acre with a spacing of approximately

13 feet between larger trees retained in the stand.

There were 24 acres in this forest area that contained approximately 162

cords to be removed. We estimated that this amounted to approximately

18 tons per acre. There was no established market for the material removed

and it was the responsibility of the company doing the work to remove all

material 4" in diameter or larger. Since an established market for the wood to

be removed did not exist, the project was offered for bids in July 1998. An

urban tree removal company was the low bidder for the project and was paid

$779.17 per acre to complete the work. The bidder used chainsaws and hand

skidding to perform the work. He reportedly intended to attempt marketing

the material removed for firewood. We do not have direct information on his

success, but anecdotal reports suggest he had difficulty with sales.

The Cheesman Reservoir – Trumbull Project

Following the disastrous impacts to Denver’s water supply from the catastrophic Buffalo Creek fire, the Denver Water Board hired a forester from the

Colorado State Forest Service and began a forest restoration and fuel break

program on its lands. A 158.6 acre thinning project was conducted during

May into July 2000 on three ponderosa pine forest areas near Cheesman

Reservoir and the village of Trumbull on the South Platte River near Deckers,

Colorado. The objectives of the project were to reduce fire hazard on these

properties and restore forest health and diversity. The project was experimental

and followed an ecological prescription based on the work of Dr. Merrill

Kaufmann (Kaufmann et al. 2001).

Chuck Dennis, forester for the Denver Water Board, translated the prescription into silvicultural marking guides for the units. The thinning was

accomplished by Brandt Logging, Inc. of Lazear, Colorado, using a Timbco

feller-buncher with sawhead, a JD 650 tracked skidder, a Kobelco loader, and

conventional log trucks. All material removed was transported to the LouisianaPacific oriented strand board plant in Olathe, Colorado, a haul distance of

Lynch and Mackes Costs for Reducing Fuels in Colorado Forest Restoration Projects
USDA Forest Service Proceedings RMRS-P-29. 2003. 171

approximately 255 miles. The logger was paid for delivered material on a per

ton basis.

Table 3 summarizes the results of the detailed cost study conducted by

Don Rogers and Dr. Dennis Lynch (2000). The average cost per acre was

$970.61. However, revenue per acre was only $479.60 resulting in an average

loss per acre of $491.01. The total project loss was $77,873.15, which was

absorbed by the logger. The agency did not charge any stumpage fee for the

material removed.

Air Force Academy Project

Following an outbreak of mountain pine beetles coupled with mortality

from dwarf mistletoe, Air Force Academy forester Jim McDermott planned a

ponderosa pine beetle tree salvage removal and forest restoration project covering 136 acres of Academy lands. For the most part, the project involved

treating small volumes per acre. Justin Anderson and Dr. Dennis L. Lynch

collected data and analyzed results from three units of this project. Two of the

units reflect beetle tree salvage removal and one unit reflects a forest restoration portion of the project. McDermott, using research reported by Dr, Merrill

Kaufmann et al. (2000), developed an ecological prescription for the restoration unit. The material in the area to be treated was of low value and generally

of poor quality, the Academy did not have labor and equipment to treat the

area, and businesses to process such material are lacking in the area adjacent to

the Academy. Therefore, the project was prepared for bid. The bidder had the

option to remove trees that might make useable products from the site without a stumpage fee. The low bid to do the mechanical treatment was $679 per

acre submitted by Morgan Timber Products of La Porte, Colorado.

Table 4 summarizes the removals and acres for the three units. The Sample

unit data came from a 10.7 acre sample of the 76 acre forest restoration unit.

The diameter classes shown in the sample data from the 76 acre unit are uncharacteristically skewed, for restoration projects, toward larger trees. This is

due to the nature of the stand treated, the prevalence of beetle attack in the

area, and the restoration prescription.

Table 3--Cheesman-Trumbull removals, acres, and costs.

Unit Acres Species 3 to 4.9" 5 to 7.9" 8 to 11.9" 12" + Tons/acre Cost/acre

Cheesman 83 Ponderosa No data 56.5% 31.8% 11.7% 14.5 $1,084.74

Trumbull #5 42 Ponderosa No data 57.1% 32.9% 10.0% 9.9 $727.77

Trumbull #6 33 Ponderosa No data 62.0% 28.4% 9.6% 14.8 $995.38

Percent removal by diameter class

Table 4--Air Force Academy removals and acres.

Unit Acres Species 3 to 4.9" 5 to 7.9" 8 to 11.9" 12" + Tons/acre

Ridge 11.2 Ponderosa 7.7% 38.6% 44.3% 9.3% 5.0

House 2.2 Ponderosa 23.6% 49.7% 22.7% 4.0% 6.0

Sample 10.7 Ponderosa 15.5% 17.8% 29.0% 37.8% 12.8

Percent removal by diameter class

Costs for Reducing Fuels in Colorado Forest Restoration Projects Lynch and Mackes
172 USDA Forest Service Proceedings RMRS-P-29. 2003.

The bidder reported that his bid was influenced by what he perceived as an

opportunity to utilize some products from the restoration unit. The restoration unit yielded a total of 14 loads of long logs hauled to Longmont, Colorado,

5 1/2 loads of short logs (8' bolts) hauled to La Porte, Colorado, and 19 loads

of wood chips hauled to Florence, Colorado. A sawmill for lumber and

dimensional products utilized the long logs, the short logs were either sawn

into dimensional timbers or used in an experimental roundwood building

project, and the wood chips were stockpiled for a biomass energy test. Volumes per load and revenues for these products are not available to us. No

additional products were realized by the bidder from the beetle tree salvage

units in this project.

Projects Where Fuels Were Left on the Site

On some forest sites, fuel loading may be quite low or sites may actually

need more organic material on the soil surface. South-facing Pikes Peak granite soils, for example, are often characterized by such conditions. Pinyon-juniper

woodland sites may also contain examples of limited surface organic material.

In such forested areas, fuel arrangement or crown closure may be of primary

concern in fire hazard mitigation planning. Under such conditions, fuels may

be removed from the overstory and distributed over the surface of the ground

without detrimental fuel loading. We conducted cost studies on several sites

with such conditions. The following is a summary of projects where data have

been collected.

Flickenstein Gulch Project

This project occurred during March 2001 in a 9.8 acre stand of mixed

ponderosa pine and Douglas-fir near Deckers, Colorado, on Denver Water

Board property. The project was an understory removal of ladder fuels. Chuck

Dennis, forester for the Denver Water Board, designed, contracted, and supervised the project. The prescription, based on research by Dr. Merrill

Kaufmann (Kaufmann et al. 2001), called for thinning from below those trees

less than 8 inches dbh. A total of 1,683 trees were cut in the unit. No material

was salvageable for products. Slash from cut trees were treated by lopping and

scattering to a depth of not greater than 18 inches. The project took 6 hours

and 15 minutes to accomplish by two men using chainsaws. The total payment for the work amounted to $990 or a cost of $100.70 per acre or $0.58

per tree. Table 5 displays the trees cut by diameter class. The forester in charge

of this project was pleased with the results.

Table 5--Trees cut by diameter class (dbh) during Flickenstein Gulch project.

< 1" 1 to 2.9" 3 to 4.9" 5 to 7.9" 8 to 11.9" 12" +

22.7% 37.6% 27.4% 10.9% 1.4% 0.1%

Percent removal by diameter class

Hydro-Ax Projects

Another way of accomplishing mechanical thinning of stands and leaving

the residues on the site is by using a Hydro-Ax machine. This machine is

Lynch and Mackes Costs for Reducing Fuels in Colorado Forest Restoration Projects
USDA Forest Service Proceedings RMRS-P-29. 2003. 173

basically a large front-end loader mounted with a massive rotary cutting head

similar to a rotary type lawn mower. The machine lifts the rotary head to a

position above the tree or shrub, achieves maximum speed with the rotating

cutting head, and then lowers the head onto the stem. The stem is then pulverized into numerous pieces of varying size depending on the material being

cut. The pieces are spread over the surrounding terrain by the force of the

cutting head. We studied this machine in the three following situations.

The first study, near Durango, Colorado, was conducted in a 37.7 acre

pinyon-juniper woodland, which included clumps of oakbrush. In this unit,

the crowns of the trees were essentially touching and very little diversity of

ground cover existed. The Hydro-ax was used to cut and pulverize trees and

oakbrush clumps to open the vegetative canopy, create vegetative diversity,

and improve fuel arrangement. The project area was on relatively flat terrain.

The bid cost for this work was $223.34 per acre.

In the second study, decadent mountain mahogany thickets near Cheesman

Reservoir were mowed with the Hydro-Ax to re-create openings within the

adjacent forest, increase sprouting and stem regeneration. The terrain was

rolling with slopes up to 30%. A total of 191.4 acres were mechanically treated

at a cost of $124.69 per acre.

In the third study, small dense ponderosa pine stands were mechanically

treated to create openings, improve fuel arrangement, and increase diversity

on 23.4 acres near Foxton, Colorado. In this case, the bid cost was $203.85

per acre.

Hydro-ax treatments may be acceptable where vegetative conditions

require fuel reductions to meet ecological prescriptions and where terrain is

such that the machine can operate safely and efficiently. The pinyon-juniper

treatment was more time consuming, caused increased wear on the machine,

and was more expensive than essentially mowing mountain mahogany

shrublands.

Summary and Conclusion

We conclude that there is no single optimal treatment system for forest

restoration fuel reduction on forest sites encountered in the ponderosa pine

and mixed conifer zones of Colorado. Each site will require individual analysis

to determine the proper ecological prescription and treatment. This will then

lead to a determination of the most effective mechanical equipment.

Costs for forest restoration will vary by ecological prescription, forest and

terrain conditions, and the availability of markets for potential products. To

date, it is clear that the material being removed from these sites is of low

quality and low or negative value. In most cases, it will cost too much to

remove material or treat areas. For these reasons, we wish to stress the point

that this material is a liability from a fuel hazard standpoint as well as from a

financial standpoint.

A well-designed timber sale may be the most economical method for an

agency to use for fuels removal if markets for products exist and processing

facilities are nearby. This was demonstrated by results of the Ponderosa Pine

Partnership and Mixed Conifer Projects, where the loggers received very low

profit margins per acre. However, in those cases the Forest Service received

stumpage payments, approximating $79 per acre and $218 per acre respectively. If the agency had chosen not to require stumpage payments for the

trees, profit margins for the loggers could have increased and made these

Costs for Reducing Fuels in Colorado Forest Restoration Projects Lynch and Mackes
174 USDA Forest Service Proceedings RMRS-P-29. 2003.

restoration projects financially more attractive. Unfortunately, after these

projects were completed, the primary processing businesses for small diameter

material were forced to close due to lack of consistent supply and high resource costs. Therefore, it is unlikely these projects could be repeated today

given this critical loss of industry.

Low profit margins and loss of industry quickly translates into non-market

alternatives where taxpayer dollars must be used to pay for fuel reduction. The

increasing size and costs of catastrophic fires leave us with nagging questions

about what to do with excess fuels from small diameter trees in Colorado.

The Front Range, Fox Run Park, Cheesman-Trumbull, and Air Force

Academy projects underscore the costs associated with fuel reduction where

markets for small diameter material are limited or lacking. In those cases, agency

costs ranged from $679 per acre to $1085 per acre to treat fuels where at least

some outlets for a portion of the small diameter material existed. What will

future costs be if such material has to be landfilled or disposed of in some

other expensive manner?

In forest areas where fuels can be left on site without adding to the hazard,

agency costs to treat ranged between $101 and $225 per acre. Certainly such

sites are limited in number and very dependent on specific forest and soil

conditions.

Continuing research in product development is crucial to a market solution

of this problem. Research to develop more efficient harvesting systems, new

wood products, and market access may help provide revenue streams that will

ultimately offset treatment costs. We believe that valued-added product development potential is likely to be highest in the utilization of the 8" to 11.9"

material found on these sites. This material, and the 12"+ diameter class

material, may be able to offset costs of cutting, handling, processing, and

transporting the low value chip material found in the 3" to 7.9" diameter

classes.

The non-market alternative is to pay millions of taxpayer dollars to restore

forests, reduce fuels, and mitigate catastrophic fire conditions while simultaneously waging very expensive and dangerous battles against large, destructive

forest fires that result in severely damaged ecosystems that must be rehabilitated, often with limited success.

Literature Cited

Gonzalez-Caban, A. and C.W. Mcketta. 1986. Analyzing fuel management cost.

Western Journal of Applied Forestry 1(4):116-121.

Kaufmann, M. R., P.J. Fornwalt, L.S. Huckaby, and J.M. Stoker 2001. Cheesman

Lake—a historical ponderosa pine landscape guiding restoration in the South Platte

watershed of the Colorado Front Range. Conference Proceedings RMRS-P-22,

pg. 9-18 Rocky Mountain Research Station. USDA Forest Service. Ft. Collins,

CO.

Kaufmann, M.R., C.M. Regan, and P.M. Brown. 2000. Heterogeneity in ponderosa

pine/Douglas-fir forests: age and size structure in unlogged and logged landscapes

of central Colorado. Canadian Journal of Forest Research 30: 698-711.

Lynch, D.L. 2001. Financial results of ponderosa pine forest restoration in

Southwestern Colorado. Conference Proceedings RMRS-P-22. Ft. Collins, CO:

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station:

141-148.

Lynch and Mackes Costs for Reducing Fuels in Colorado Forest Restoration Projects
USDA Forest Service Proceedings RMRS-P-29. 2003. 175

Lynch, D. L. 2000. Summary report of the forest restoration thinning project on

Denver Water Board property at Cheesman Reservoir and Trumbull sites. Dept. of

Forest Sciences. Colorado State University. Ft. Collins, CO.

Lynch, D.L., W.H. Romme, and M.L. Floyd. 2000. Forest restoration in southwestern

ponderosa pine. Journal of Forestry 98(8): 17.

Lynch, D. and C. Jones. 1998. Summary report of the mixed conifer demonstration

project. Dept. of Forest Sciences. Colorado State University. Ft. Collins, CO.

Preston, M. and C. Garrison. 1999. The ponderosa pine forest partnership: community

stewardship in Southwestern Colorado. Montezuma Federal Lands Program.

Montezuma County, Cortez, CO.

Richard, T. and S. Burns. 1999. The ponderosa pine partnership. Office of Community

Services, Ft. Lewis College, Durango, CO.

Romme, W.H., H.D. Grissino-Mayer, M.L. Floyd, and D. Hanna. 1999. Landscape

condition analysis for the South Central Highlands section: Ponderosa pine forests.

Unpublished final report to the San Juan National Forest, Durango, CO.

Costs for Reducing Fuels in Colorado Forest Restoration Projects Lynch and Mackes
176 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 177

The Effects of Fire on Hiking Demand:

A Travel Cost Study of Colorado and Montana

Hayley Hesseln1, John B. Loomis2, and Armando González-Cabán3

Abstract—Surveys were conducted on 33 sites within National Forests in Colorado

and Montana to test how forest fires affected recreation demand in the two states.

Data were collected on the actual number of visits and on the intended number of

visits if the area had been subject to a recent high intensity crown fire, a recent

prescribed fire, or an old crown fire (all depicted in photos). A travel cost model was

estimated by pooling actual and intended visitation responses in both states. Results

indicate that Montana hikers take slightly more trips but have lower net benefits or

consumer surplus ($12 per trip) than do Colorado visitors ($55 per trip). Also, the

demand functions do not react similarly to prescribed fires. Whereas annual values

in Colorado increase over time, there were no significant changes in visitation or net

benefits for Montana respondents. However, demand functions do react similarly in

response to crown fires, resulting in a decrease in visitation and value over time.

This latter result provides evidence in support of increased fuels management as

outlined by the National Fire Plan.

Introduction

Fire managers and recreation managers need cost-benefit information to

determine the most effective and efficient fuels management techniques,

such as mechanical treatments or prescribed burning. In addition to using

accounting costs, a complete economic analysis should include social costs

and benefits associated with fire. For example, it may appear that prescribed

burning is more cost effective than mechanical treatments given the accounting costs per acre. If burning generates significant negative social impacts in

the way of increased health costs from smoke and diminished aesthetics, the

economic cost of burning may be higher than the cost of mechanical treatments. It is important to incorporate social values when determining fire management methods, particularly in high-use recreation areas. However, this is

difficult given that there are little data available to estimate fire effects on nonmarket amenities.

While a few past research efforts have been conducted to assess the effects

of fire on recreation (Englin et al. 1996), much of this work has been in

Canadian boreal forests or does not include effects on popular activities including hiking and mountain biking. Furthermore, there is little quantitative

information available for fire managers to evaluate the differential effects that

wild and prescribed fire have on recreation visitation and values. Several studies have been done that indicate fire effects cause decreases in aesthetic value.

Vaux et al. (1984) used the Contingent Valuation Method (CMV) to estimate

the economic effects of burned areas on recreation. Results indicated that

higher intensity fires negatively affected recreation values. Flowers et al. (1985)

conducted similar research with respect to the northern Rocky Mountains

1Associate Professor, School of Forestry,

University of Montana, Missoula, MT.

2 Professor, Department of Agricultural

and Resource Economics, Colorado State

University, Fort Collins, CO.

3Economist, USDA Forest Service, Forest

Fire Lab, Pacific Southwest Research

Station, Riverside, CA.
178 USDA Forest Service Proceedings RMRS-P-29. 2003.

and determined that there was no clear consensus regarding the treatment of

fire duration. Englin et al. (1996) and Boxall et al. (1996) used the travel cost

method to assess changes in value on canoeing in Manitoba, Canada. The

travel cost method (TCM) was used by Loomis et al. (2001) to evaluate fire

effects on hiking and biking in Colorado. They found that there were differential effects on hiking and biking visitation as a result of different fire ages

and the presence of crown fires. Similarly, consumer surplus for bikers was

indirectly affected by crown fire. Finally, Englin et al. 2001 provide a comparative analysis of fire effects over time in the Intermountain West.

The Colorado survey was replicated in Montana and comparisons were made

between states to test whether results are generalizable between states. Therefore the same survey and survey methodology were used to test the differences in fire effects on recreation between Montana and Colorado. Because

the survey was designed to estimate demand for recreation in National Forests

based on actual trips taken and contingent behavior based on three fire

scenarios, the Lolo, the Bitterroot, the Flathead, and the Helena National

Forests in Montana were used. The survey instrument was identical except for

years trips were taken. An overview is provided of the methodology used in

both states, followed by a discussion of the model and hypotheses. Finally,

results of the regression models and findings are presented.

Methodology

The travel cost method (TCM) was used to estimate the demand for recreation in Montana as based on the survey by Loomis et al. (2001) conducted in

Colorado. Using the resulting demand curve, consumer surplus or net benefits per individual, per trip can be calculated by integrating the area under the

demand curve. Actual and hypothetical trips were measured as a function of

site characteristics including elevation and species, as well as fire characteristics

including the presence of a crown fire, fire age, and percentage of burn observable from the trail, demographics, and travel cost information.

Travel cost data included the cost of gas, camping, and other travel related

expenditures. The treatment of travel time is often problematic in the TCM.

Omitting travel time can lead to specification errors and an underestimation

of the true value of the recreation trip (Allen et al. 1981). A traditional solution to this problem has been to value travel time as a fraction of the wage rate

and add it to the monetary cost of travel to create one composite variable.

This approach is taken here. To minimize the multicolinearity of travel cost

and travel time, travel time is multiplied by 40% of the respondent’s wage rate.

This approach is used by federal agencies in TCM (U.S. Water Resources

Council 1983). Thus the travel cost variable and hence consumer surplus will

not exhibit omitted variable bias, but it should not be strongly influenced by

the particular value of travel time chosen.

A count data specification of the TCM demand model is employed since

the number of trips taken (whether actual or intended) is a non-negative integer. Typical count data specifications include the Poisson and Negative Binomial

(Creel and Loomis 1990). These count data models are equivalent to a semilog of the dependent variable functional form.

Fire Effects TCM

To test differences between Colorado and Montana, a pooled interaction

model is used with intercept shifters and slope interaction terms for Colorado

observations. The model is specified by equation 1.

Hesseln, Loomis, and Gonzalez-Caban The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana
USDA Forest Service Proceedings RMRS-P-29. 2003. 179

TRIPS = b 0 + b 1 (Respondent’s age)+ b 2(Burn observed) + b 3(Crown fire)

+ b 4(Time since prescribed fire) + b 5

(Time since crown fire) + b 6(Elevation) + b 7

(Travel cost) + b 8(Acres) + b 9(Actual vs. hypothetical trips)

+ b 10(Income) + b 11(Gender) + b 12(Lodgepole pine) + b 13(Group size)

+ b 14(Travel time available) + b 15(Value of aging prescribed fire)

+ b 16(Value of crown fire) + b 17(Colorado) + b 18(Travel cost for

Coloradoan) + b 19(Crown fire in Colorado) + b 20(Time since prescribed

fire in Colorado) + b 21(Time since crown fire in Colorado) + b 22(Value

of recovering prescribed fire in Colorado) + b 23(Value of crown fire in

Colorado) + b 24(Dirt road access) + b 25(Value of aging crown fire)

+ b 26(Value of aging crown fire in Colorado) [1]

Model variables and definitions are given in table 1.

Table 1—Model variables and descriptions.

Variable Description

Trips taken Trips planned and trips taken by the respondent.

Acres Size of fire in acres.

Age Respondent’s age.

Burn observed Percentage of fire observable on trail.

Crown fire crown fire = 1, no crown fire = 0.

Time since prescribed fire Number of years since low intensity fire.

Time since crown fire Number of years since stand replacing wildfire.

Dirt road Access was on dirt road = 1, otherwise no = 0.

Elevation Trailhead elevation above sea level.

Travel cost Individual share of travel costs plus value of travel time to site.

Actual vs. hypothetical Actual trip taken = 0, intended trip = 1.

Income Household income of survey respondent.

Gender Male = 1, Female = 0.

Lodgepole pine Lodgepole pine present = 1, other species = 0

Group size Number of people in group.

Travel time available Total time available for non-winter vacation (weekends plus paid vacation).

Value of aging prescribed fire Interaction between travel cost and fire age to test whether individual net

benefits per trip changes as prescribed fires recover.

Value of crown fire Interaction variable between total cost and crown fires to test the effects of

crown fires on individual net benefits.

Colorado Colorado respondent = 1, Montana respondent = 0.

Travel cost for Coloradoan Interaction between Colorado and travel cost to test differences in individual

net benefits between Colorado and Montana respondents.

Crown fire in Colorado Interaction between Colorado and Crown fire to test how crown fire

influences trips taken in Colorado.

Time since prescribed fire in Interaction between Colorado and time since prescribed fire to test how

Colorado trips differ according to fire age.

Time since crown fire in Colorado Interaction between Colorado residents and areas recovering from crown

fires to test how the number of trips taken changes.

Value of crown fire in Colorado Interaction variable between total cost, crown, and the dummy for Colorado

to test the effects of crown fires on consumer surplus for Coloradoans.

Value of recovering prescribed Interaction between Colorado, total travel cost, and areas recovering

fire in Colorado from prescribed fire to test whether individual net benefits change.

Value of aging crown fire Interaction between travel cost, presence of a crown fire, and fire age to test

how value changes in response to recovering crown fires.

Value of aging crown fire in Interaction between travel cost, presence of a crown fire, and fire age to test

Colorado how value changes in Colorado in response to recovering crown fires.

The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana Hesseln, Loomis, and Gonzalez-Caban
180 USDA Forest Service Proceedings RMRS-P-29. 2003.

The model is specified to calculate consumer surplus and to indicate whether

fire effects have an influence on visitation and value of trips taken, and how

this differs between Colorado and Montana. Consumer surplus is the area

under the demand curve between current travel cost and the choke price that

reduces trips to zero. Because a count data model is used which is equivalent

to a semi-log demand function, consumer surplus is calculated as 1/b Travel Cost

(Loomis et al. 1999). To calculate the consumer surplus per trip for individual

Colorado trips, the coefficient for the interaction term is included, which is

specified by equation 2.

1/( b 7

 + b 18) [2]

To test the effects of fire age on consumer surplus we combined travel cost

variables with time since prescribed fire for both Montana and Colorado. Specifically, if fire age has an effect on the price slope of the demand curve, the

coefficient b 15 will not be equal to zero. Equations for consumer surplus per

trip for Montana and Colorado are given by equations 3 and 4.

1/( b 7

+ b 4*Time Since Prescribed Firet) [3]

1/(( b 7

+ b 18+ ( b 4*Time Since Prescribed Firet) +

( b 22*Value of Recovering Prescribed Fire in Colorado)) [4]

T-tests are used to test whether there are significant positive or negative

effects of the fire variables. Specifically, time since fire age, presence of a crown

fire, and time since crown fire, crown fire in Colorado, time since prescribed

fire in Colorado, and time since crown fire in Colorado are of interest. Finally,

regression results are used to estimate the effects of fire on value per day and

the number of trips taken over time. We note that our demand model does

not explicitly include a variable for the price or travel cost to substitute sites.

Therefore the absolute value of our estimates of visitor net benefits may be

overstated.

Data Collection

Sample Design

Three National Forests in Colorado were selected that provided a sample of

the possible combinations of fire age and acres burned and were logistically

functional to sample. The Arapaho-Roosevelt, Gunnison-Uncompaghre and

Pike-San Isabel National Forests were chosen. This provides two Front Range

National Forests and one interior National Forest. Four National Forests in

Montana were selected for this study based on fire history and recreation use.

They include the Bitterroot National Forest, the Flathead National Forest,

the Lolo National Forest, and the Helena National Forest. Each forest

included areas that experienced fire in 2000 and areas without fire to be used

as control sites. The mean fire size was 27,000 acres while the median fire size

was 1,200 acres. With respect to fire age, the oldest actual fire was 24 years

old and the newest, one year. Sites sampled that were not affected by fire were

coded as –50 years.

Sampling occurred over 35 days during the main summer recreation season

in Colorado in 1998. A total of 10 sites over the three National Forests were

sampled. This schedule generally allowed one sampling rotation of two days

(one weekday and one weekend day) at nearly all recreation sites during July

and August. Twenty-two Montana sites were sampled for a total of 25 days in

2000. Because of fire activity in the Bitterroot Valley, and in Montana in

Hesseln, Loomis, and Gonzalez-Caban The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana
USDA Forest Service Proceedings RMRS-P-29. 2003. 181

general, all recreation areas were closed for use. Prior to closure, sampling

occurred over 11 days. After fire restrictions were relaxed, sampling continued an additional 14 days. The survey was concluded in 2001 after all

surveys were distributed. Sampling occurred over 34 days between June

and August inclusively and was conducted on both weekdays and weekends in both years.

Survey Protocol and Structure

The interviewer intercepted individuals at each trailhead as respondents were

going to or coming from the trails. The interviewer introduced herself and

gave her university affiliation and purpose. Respondents were told they could

complete the survey on site, or take it home and mail it back in a postage paid

return envelope included in the package. Surveys were disseminated to individuals 18 years or older.

Respondents were asked to provide their primary recreation activity and

important attributes of the site. Next they were asked to provide travel time,

travel distance, and travel cost to the site. Travel cost included gas cost only.

Individuals were then asked to provide the number of trips taken to the site, as

well as planned trips for the remainder of the year. Finally, respondents were

asked how their visitation might change if the cost of their trip increased.

The following section of the survey presented three fire scenarios using

color photographs of the following:

• High-intensity crown fire: blackened, standing trees with little greenery.

• Light prescribed burn: underbrush burned, trees burned on the lower

portion of the trunk, reddish needles on lower branches, green needles

on the majority of the trees.

• High-intensity 20-year-old burn: standing dead trees, white trunks,

downed trees mixed with new greenery.

Contingent trip behavior analysis was based on photos that depicted trails

in such conditions. Respondents were asked how their visitation to each site

would change if half the trail resembled the photo. This enabled efficient

conveyance of the effects that high-intensity crown fires, prescribed fires, and

older burns have on recreation demand.

Contingent behavior was also assessed based on price using increased trip

costs ($3, 7, 9, 12, 15, 19, 25, 30, 35, 40, and 70). Respondents were asked

to record the number of trips they would take if travel costs were increased.

This provided additional price variability to supplement the natural variability

in travel costs due to different originations.

Site characteristics were included to control for variability among sites.

Attributes were chosen based on those that were significant in past forest

recreation studies (Englin et al. 1996). Site characteristics included elevation,

elevation gained on trail, miles of dirt road with respect to access, and the

number of recreation activities occurring on the site. Fire history information

included fire age, size of burn, and intensity. Finally, vegetation type and the

presence of water was recorded.

Results

In Colorado, there were 14 refusals out of 541 contacts made. A total of

527 surveys were handed out. Of these, 354 were returned after the reminder

postcard and second mailing to non-respondents for an overall response rate

The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana Hesseln, Loomis, and Gonzalez-Caban
182 USDA Forest Service Proceedings RMRS-P-29. 2003.

of 67%. The total number of contacts made in Montana was 1,074 of which

there were 24 refusals. In total, 1,050 surveys were disseminated, and 559

were returned after first and second postcard reminders were mailed. The

overall response rate was 53%.

Of the visitors in Colorado sampled at the trailheads, 59% were hiking and

30% were mountain biking. The remainder of visitors (11 percent) were horseback riding or on motorized vehicles. Of the visitors to the 22 sites in Montana, approximately 78% were hiking, camping, and sightseeing. The next

largest categories were biking at 10%, fishing at 7%, and swimming and water

related activities at 5%. Only hikers and mountain bikers from each state were

included in the analysis for consistency.

In Colorado, visitors drove an average of 77 miles (one-way) and their

share of the gasoline costs was $12. In Montana the average distance traveled

was 98 miles (one-way) and the average individual cost of gasoline was $9.50.

The demographics of the Colorado sample indicated that 44% of respondents were female, and that the sampled population had an average age of

36.5 years and education level of 16.3 years. The typical household earned

$67,232. Demographics for Montana indicated that 49% of respondents were

female, the sampled population had an average age of 39 years and education

level of 16.0 years. Average household earnings were $55,135. Averages for

Colorado and Montana are summarized in table 2.

Significant fire variables are displayed in table 3; the model is significant

with a p-value of 0.000. The model has an adjusted R-squared value of 25%.

There is a significant difference between trips taken in Montana (10 per individual per site) and trips taken in Colorado (7 per individual per site).

Travel cost including the value of travel time is also negative and significant

for Montana at p <0.01. Surprisingly, total time available for travel had a negative effect on the number of trips taken and was significant at p <0.01. With

respect to site characteristics, LP (lodgepole pine) had a significantly negative

effect on the trips taken whereas site elevation was positive and was significant

at p <0.01. While aspen, Douglas-fir and ponderosa pine were also evaluated,

they were highly correlated with lodgepole pine and therefore omitted from

the model. As expected, the coefficient on dirt road access was negative indicating that people take fewer trips if access is not paved. The Actual-Hypothetical variable was positive and significant indicating that

respondents overstated the number of trips they would take indicating hypothetical bias for contingent behavior estimates versus the number of actual

trips taken.

Table 2—Descriptive statistics of travel survey for Colorado and Montana.

Variable Colorado Montana

Trip characteristics

Travel distance (one way) 77 miles 98.6 miles

 Average gas cost per respondent $12 $9.50

Hikers 59% 77%

 Other 41% 23%

Demographics

Percent females 44% 49%

 Age 36.5 years 39 years

 Education 16.3 years 16 years

Household income $67,232 $55,135

Hesseln, Loomis, and Gonzalez-Caban The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana
USDA Forest Service Proceedings RMRS-P-29. 2003. 183

Significant demographic variables include income (p <0.01) and gender

(p <0.01), which were negative, and group size (p <0.01) and respondent’s

age (p <0.01), which were positive. As income increases, the number of hiking

trips taken tends to decrease. The negative coefficient on gender indicates

that females take more trips. The positive coefficient on age indicates that

older people take more trips. Surprisingly, group size was positive indicating

that larger groups take more trips.

Consumer surplus

Using the coefficient for travel cost (TC), the consumer surplus per trip for

Montanans is $11.54 with a 95% confidence interval of $10.89 to $12.28.

Using the coefficients for travel cost and the interaction variable between

Colorado and total cost to calculate the consumer surplus per trip, the average

net benefit for Coloradoans was $54.59 per individual per trip with a 95%

confidence interval of $33.79 to $141.94. These results are similar to other

studies such as Walsh et al. (1992), who estimate the national average value of

hiking to be $29, and Rosenberger and Loomis (2000), who updated the

Walsh study arriving at a value of $37.

The effects of fire age on consumer surplus were tested using the value of

recovering prescribed fire in Montana and Colorado. Change in consumer

surplus was significant in Colorado. As prescribed fires recover, net benefits

per individual in Colorado increase. For example, a 25-year-old fire would

result in consumer surplus of $89, and for a 50-year old fire, consumer surplus

increases to $242. While the value of a recovering prescribed fire was significant in Colorado (p <0.01), the value of recovering prescribed fires in

Montana was not significant indicating that consumer surplus in Montana is

not affected by time. The increase in annual value for Colorado is 346% over

50 years, whereas over the same time frame, the increase in annual value in

Montana is 1.7%. Changes in visitation and value are shown in table 4.

The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana Hesseln, Loomis, and Gonzalez-Caban

Table 3—Significant Montana and Colorado fire variables. (Trips = dependent variable.)

Coefficient Significance (p)

Acres -0.0000 < 0.00

Crown fire 0.2107 < 0.00

Time since prescribed fire -0.0036 < 0.00

Time since crown fire 0.0106 < 0.00

Travel cost -0.0866 < 0.00

Value of crown fire 0.0002 < 0.00

Value of recovering crown fire 0.0003 < 0.11

Coloradoan -3.4067 < 0.00

Travel cost for Coloradoan 0.0683 < 0.00

Time since prescribed fire in CO -0.0155 < 0.00

Crown fire in Colorado -0.1176 < 0.16

Time since crown fire in CO -0.0005 < 0.20

Value of recovering crown fire in Colorado 0.0005 < 0.08

Value of crown fire in Colorado 0.0113 < 0.03

Value of recovering prescribed fire in Colorado -0.0002 < 0.00

R-squared 26%

Adjusted R-squared 24%
184 USDA Forest Service Proceedings RMRS-P-29. 2003.

Alternately, the economic effects as a result of a crown fire were statistically

significant for both Montana and Colorado, yet the difference in visitation

between states was not significant. Given a crown fire, annual individual benefits decrease in Montana by 86.7% and 69.3% in Colorado. These results are

statistically significant, and have implications from both policy and management perspectives.

Fire effects

The time since prescribed fire had a slightly positive effect on visitation in

Montana and Colorado, and was significantly different between states

(p <0.00). However, absolute differences are small enough to have no policy

Table 4—Visitor use and benefits with fire age.

Prescribed fire 0 years 25 years 50 years % change

Colorado

Trips 10.28 10.30 10.33

Value $54.59 $89.10 $242.20

Annual value $561.18 $917.61 $2,501.92 +346 %

Montana

Trips 11.28 11.30 11.45

Value $11.54 $11.54 $11.54

Annual value $130.17 $130.40 $132.13 +1.7%

Crown fire 0 years 25 years 50 years

Colorado

Trips 10.28 10.28 10.28

Value $54.59 $25.66 $16.77

Annual value $561.18 $263.78 $172.40 -69.3%

Montana

Trips 10.25 9.98 9.71

Value $11.54 $2.83 $1.62

Annual value $118.28 $28.24 $15.73 -86.7%

implications when considered alone. For example, trips taken in Montana

increases from 11.25 with no fire, to 11.34 with a 25-year-old fire. For a

50-year-old fire, the average number of trips increases to 11.45. Over the

same period, trips taken in Colorado increases from 10.28 to 10.30 and 10.33.

Thus, the outward shift of the demand curve over the fire recovery interval

indicates a very small increase in visitation.

The presence of a crown fire was positive and statistically significant

(p <0.00), yet there was no difference between states. Trips in Colorado increase from 10.28 to 11.38 given a crown fire, and from 10.25 to 11.48 in

Montana. The effect on visitation of time since crown fire was negative and

significant for both states (p <0.01). The interaction term indicates that older

crown fires receive fewer visits than newer crown fires. Trips to areas with

crown fires that are 25 and 50 years old decrease slightly from 9.98 to 9.71 in

Montana with no change in Colorado. This may be explained by the initial

interest in seeing effects of severe fires.

Hesseln, Loomis, and Gonzalez-Caban The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana
USDA Forest Service Proceedings RMRS-P-29. 2003. 185

Conclusion

The average number of individual trips taken per site in a no-fire situation

in Colorado was 10.28 with individual net benefits per trip of $55. The number of individual trips taken per site in Montana was similar at 10.25 with

individual net benefits of approximately $12.

With respect to fire effects, findings indicate that wild and prescribed fires

have varying effects on recreation demand and value in each state. When visitation and value are considered in conjunction, however, prescribed fires result in increased annual values in Colorado (346%). While this is significant,

the change in Montana is not (1.7%). Alternately, crown fires in both states

result in decreased annual values of 69.3% in Colorado and 86.7% in Montana. While respondents in each state do not behave similarly with respect to

prescribed burning, these results provide support for the National Fire Plan

(USDI/USDA 2002). In Colorado, prescribed burning not only increases

the annual value of recreation over time, but may mitigate increasing social

costs resulting from crown fires. In Montana, whereas prescribed fire does not

increase value over time, it may have value in terms of mitigating the negative

effects on annual recreation values as a result of crown fires.

Because of the rapid pace of education in natural resources, particularly

with media coverage of fire, it would be useful to conduct the same survey in

the future to test differences over time. While results may be used to generate

the social costs of prescribed fires, such costs may fall over time with education and increased knowledge, and may have a different pattern in other states.

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fire on the economic value of forest recreation in the intermountain west: preliminary

results. Gen. Tech. Rep. PSW-GTR-173. Albany, CA: U.S. Department of

Agriculture, Forest Service, Pacific Southwest Research Station: 199-208.

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use values. Gen. Tech. Rep. RMRS-GTR-72. Fort Collins, CO: U.S. Department

of Agriculture, Forest Service, Rocky Mountain Research Station. 59 p.

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land implementation studies. Washington DC: U.S. Water Resources Council.

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Experiment Station. 13 p.

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demand studies: 1968-1988. Water Resources Research. 28(3): 707-713.

Hesseln, Loomis, and Gonzalez-Caban The Effects of Fire on Hiking Demand: A Travel Cost Study of Colorado and Montana
USDA Forest Service Proceedings RMRS-P-29. 2003. 187

Linking GIS and Recreation Demand Models

to Estimate the Economic Value of Using Fire

to Improve Deer Habitat

John Loomis1, Armando González-Cabán2, Dana Griffin1,

and Ellen Wu1

Abstract—This research combines a Geographic Information System (GIS) based

model of deer habitat response to fire with a travel cost method recreation demand

model to value the deer hunting benefits of prescribed burning in the San Jacinto

mountains of southern California. A statistically significant effect of fire on deer harvest was determined in the GIS based and time-series based production function

models. Using the recreation demand model, we estimated the net economic value

to hunters of $257 per additional deer harvested. While the initial deer hunting

benefit response to the current magnitude of prescribed burning of 1,100 acres ranges

from $4,112 to $8,481 depending on the production model, the incremental gains

for additional 3,700 acres of prescribed burning are quite similar across production

models.

Introduction

This research compares two models for evaluating the effectiveness of

prescribed burning for increasing deer habitat. We also provide a

benefit-cost comparison for the San Jacinto Ranger District (SJRD) in the San

Bernadino National Forest located in southern California. The methodological contribution begins to answer the challenge posed by Hesseln (2000) in

her recent review of the economics of prescribed burning. She stated: “There

is a lack of economic models to evaluate short- and long-term ecological benefits of prescribed fire. Without understanding the relationship between economic outcomes and ecological effects, it will be difficult to make effective

investment decisions. Research should focus on defining a production function to identify long-term relationships between prescribed burning and

ecological effects. Identifying production functions relationships will form the

basis for future cost-benefit analysis with respect to prescribed burning”

(Hesseln 2000: 331-332). Our study demonstrates two different approaches

to estimating production relationships between prescribed burning and deer

harvest using time series data and Geographic Information System (GIS)

approaches. The production models are linked to the recreational hunting

valuation model by including deer harvest as a demand shift variable in the

recreational hunting valuation model.

Study Area

The San Jacinto Ranger District (SJRD) is located in Southern California’s

San Bernardino National Forest near Palm Springs. As noted by the USDA

1 Department of Agricultural and

Resource Economics, Colorado State

University, Fort Collins, CO.

2 Forest Fire Lab, Pacific Southwest

Station, USDA Forest Service, Riverside,

CA.
188 USDA Forest Service Proceedings RMRS-P-29. 2003.

Forest Service, “Some of the best deer hunting in Riverside County is found

in this area” (Gibbs et al. 1995: 6). The SJRD is an ideal area to demonstrate

and compare different approaches to estimating a production function

between prescribed burning and deer harvest because prescribed fire has been

used for more than 20 years to stem the long-term decline in deer populations

since the 1970s (Paulek 1989, Gibbs et al. 1995). Previous research on prescribed burning shows that fire enhances deer habitat and populations

(California Department of Fish and Game 1998) but the economic benefits

have not been quantified. The results of our analysis should be of some policy

relevance as the SJRD plans to increase the amount of prescribed burning by

50% to 100% over the next few years (Gibbs et al. 1995).

In general, Southern California is characterized by a Mediterranean

climate, with hot and dry summers and cool, moist winters. There is a significant range of variation in temperatures and local site conditions in the ranger

district in the San Jacinto and Santa Rosa Mountains. Elevations in these ranges

reach 10,800 feet. The dominant vegetation within the SJRD below 5,000

feet is chaparral. Annual rainfall for the chaparral biome is approximately 15

to 16 inches. Areas above 5,000 feet tend to be dominated by hardwoods and

conifers such as live oak and Douglas-fir with annual rainfall of up to 30 inches.

Within the San Jacinto Ranger District, the land is primarily managed by

the USDA Forest Service, with small amounts of land administered by the

State of California such as the Mount San Jacinto State Park. The land within

the San Jacinto Ranger District is an area that evolved with fire as a natural

environmental factor. Declining abundance of successional vegetation

communities is considered to have the greatest long-term effects on deer populations (California Department of Fish and Game 1998). Historically, fire,

either prescribed or natural, has been the primary mechanism for establishing

these vegetation communities. Studies in California have noted that after a

burn, increased deer numbers can be attributed to individuals moving into

the area to feed (Klinger et al. 1989). These increased deer numbers are thought

to arise due to increased forage quality and increased fawn survival rates in the

recently burned areas. The California Department of Fish and Game has noted

a significant increase in buck harvest from 1987 to 1996 in hunt zones that

had large fires, versus hunt zones that did not have large fires (California Department of Fish and Game 1998). To improve deer habitat in California,

controlled burning has been used in all the major parks and forests for more

than a decade (Kie 1984).

Two Production Function Modeling Approaches

The purpose of this study is to test whether prescribed burning has a systematic effect on deer harvest. By examining prescribed burning on deer

harvest with two different production models, a macro or aggregate time

series approach and a micro, spatial approach (e.g., GIS) can be compared.

Using a macro time series approach, we would be able to test the effects of

fire, prescribed and natural, across the entire study area over a 20-year time

period. Using a micro GIS approach provides greater spatial detail, such as the

influence of a meadow or ridge, but this micro data is not available for the

20-year time period. Thus each approach to estimating the production function has its relative strengths and weaknesses.

Estimating a production function that relates deer harvest to acres of prescribed burning must also control for other inputs that influence the production

Loomis, González-Cabán, Griffin, and Wu Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat
USDA Forest Service Proceedings RMRS-P-29. 2003. 189

of deer for harvest. This includes wildfire, elevation (used as a proxy for vegetation data that was incomplete), rainfall, temperature, and distance to roads.

Thus, multiple regression is an appropriate technique.

Time Series, Macro Scale Production Function

The macro approach is based on a time series regression model to test for a

relationship between deer harvest in the SJRD and prescribed fire, controlling

for other independent variables such as annual precipitation and temperature

during the hunting season. For this approach we used a dataset for SJRD,

provided by the California Department of Fish and Game (CDFG) and the

USDA Forest Service. The fire records provided data from 1979 for wildfire

and prescribed burns within the San Jacinto Ranger District. This ranger district represents the majority of publicly accessible land for deer hunting in

Riverside county. Deer harvest data from 1979 were provided by CDFG. The

full model is given by equation 1.1. SJRD Time Series Production Function

Model:

SJRD Deer harvest in year t

 = func (RXFIREt, WILDFIREt,

TOTPRECIPt

, OCTTEMPt, YEARt) [1.1]

Where:

RXFIREt

 = the acres of prescribed fire in year t

WILDFIREt

 = the acres of wildfire in year t

TOTPRECIPt

 = the sum of precipitation for year t

OCTTEMPt

 = temperature in October during the hunting season

YEARt = a trend variable, with 1979 = 1, 1980 = 2, ... 1998 = 19

We estimate a non-linear form of equation 1.1 using the log-log form. The

log-log form allows us to interpret the coefficients for fire effects as elasticity’s,

i.e., the percent change in deer harvest with a 1% change in acres burned.

To model the aggregate harvest for all of the San Jacinto Ranger District,

the dependent variable is the total number of deer harvested in year t. The

value of the dependent variable is a relatively large number and varies between

80 to 157 deer in any given year. Because of the relatively large values for the

dependent variable, Ordinary Least Squares (OLS) is an acceptable approach

for the macro time-series modeling.

Micro GIS Approach to Estimating the Production Function

The approach taken in this study uses a geographic information system

(GIS) for estimating the deer harvest-fire production relationship. With the

GIS approach, the study area is divided into 37 individual hunting zones

delineated by California Department of Fish and Game (rather than treating

the entire SJRD as one unit). These hunt areas are defined by topographic

features such as steep ridgelines or developed features such as towns or major

roads. This allowed for the incorporation of other influences on deer harvest

that varied spatially across individual hunting areas such as distance to roads

and elevation. Because past research indicates that use of burned areas by deer

increases dramatically during the following years, (Klinger 1989) a lagged

model as shown in equation 1.2 is estimated. This model tests for these effects

during the following years by using a lag on the fire variables.

The first step in the GIS analysis was to identify the necessary layers needed

to run a regression between deer harvest and fires. A harvest layer was

Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat Loomis, González-Cabán, Griffin, and Wu
190 USDA Forest Service Proceedings RMRS-P-29. 2003.

constructed, which contains deer harvest by hunt zones and serves as the

dependent variable. Then layers were added for the independent variables

including acres of prescribed burning, wildfire acres, average elevation, temperature, distance to trails, distance to dirt roads from each hunting zone, and

distance to wildfires from each hunting zone. Vegetation type would have

been desirable, but this information was incomplete and will not be completed for the entire area until well into the future. Thus elevation was partially used as a proxy for vegetation composition. We developed two models

for the harvest areas to account for the non-uniform size of each hunting

zone: (1) include the size of the harvest area as a separate independent variable and use total acres of an area burned and, (2) transform the dependent

variable into deer harvest per acre, then use an OLS regression. The total area

model (with deer harvest a function of total size of fire, including lags) is

shown in equation 1.2:

Deer harvest in areai in year t = func (Avg_Elevi

, Ltotal_Wildfirei t,

Ltotal_Wildfirei t-1, Ltotal_Wildfirei t-2, Ltotal_Wildfirei t-3, Ltotal_Rxfirei t

,

Ltotal_Rxfirei t-1, Ltotal_Rxfirei t-2, Ltotal_Rxfirei t-3, Ldirt_distancei

,

Ltrail_distancei

, LHvst_Areai, Oct_Temp t, Year t) [1.2]

Equation 1.2 was estimated using a count data model instead of OLS

regression because at the micro level harvest in any limited spatial unit is a

small non-negative integer variable. Therefore count data models are statistically more efficient because such models are based on probability distributions that have mass only at nonnegative integers (Creel and Loomis 1990).

This is certainly the case for deer harvests as hunters cannot harvest a fraction

of a deer and the number harvested in each unit is typically 0,1,2,3... rather

than 10 or 50. One of the simplest count distributions is the Poisson process.

Given the stringency of the mean-variance equality restriction imposed by the

Poisson, a more generalized count model like the negative binomial is often

more consistent with the data. The negative binomial version relaxes the meanvariance equality of the Poisson. Both the Poisson and the negative binomial

yield the equivalent of a semi-log form where the log of the dependent variable is regressed against the explanatory variables.

An alternative specification to account for the different size harvest areas

involved transforming the dependent variable into deer harvest per acre. This

results in equation 1.3, which is estimated using ordinary least squares regression since this dependent variable is continuous and is not restricted to integer

values:

Log Deer_harvest per acre in yeari t = func (Avg_Elevi

, Ltotal_Wildfirei t,

Ltotal_Wildfirei t-1, Ltotal_Wildfirei t-2, Ltotal_Wildfirei t-3, Ltotal_Rxfirei t

,

Ltotal_Rxfirei t-1, Ltotal_Rxfirei t-2, Ltotal_Rxfirei t-3, Ldirt_distancei

,

Ltrail_distancei

, LHvst_Areai, Oct_Tempt, Year t) [1.3]

Details of GIS Based Micro Regression Variables

Elevations are based on USGS digital elevation models and act as a proxy

for vegetation types, which were not available. However, we do not have an

expected sign on elevation, but include it to control for elevation differences

among the 37 individual hunting areas within the San Jacinto Ranger District.

Based on the literature reviewed above, wildfire and prescribed fire are expected to have a positive sign. The distances to road and trail variables are

based on the distances from a central point in each hunting zone. Two

Loomis, González-Cabán, Griffin, and Wu Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat
USDA Forest Service Proceedings RMRS-P-29. 2003. 191

arguments can be made about the sign of this variable, therefore the expectation is left to be ambiguous. One argument is based on accessibility for hunters, where being close to either a trail or road would make hunting easier and

more desirable, which would positively effect deer harvest. The second argument is based on the intrusion of the road or trail possibly fragmenting deer

habitat. This perspective would lead to a decline in deer harvest because roads

cause a break in habitat and pose as a threat from cars.

The distance to fire variable is based on distance from a central point in

each hunting zone to the closest fire in that time period. This variable’s sign

may be either positive or negative.

Harvest area, which takes into account the size of each hunting zone, is

expected to have a positive sign. The argument here is that as hunting areas

become larger, then the amount of deer habitat increases, which attracts more

deer and increases the probability of hunter success. October temperature and

year are the other variables used in the models. October is when hunting

season is open and, based on hunter’s surveys, when temperatures are high

the deer tend to bed down and seek cover. Therefore, harvest rates decline,

which gives the October temperature a negative sign. Year is a trend variable

to capture any temporally varying effects and we do not know whether it

would be positive or negative. Table 1 summarizes the description of the variables and their expected sign, if any.

Table 1—Description of GIS based micro regression variables.

Variable Description Expected sign on coefficient

Deer harvest The dependent variable; the number of deer harvested in a

designated hunting zone.

Avg_Elev Average elevations, based on USGS Digital Elev. Model and No expectation

re-classed into elevation categories.

TOTWFIRES Total wildfires in a particular year within the San Jacinto Ranger +

District (-1,-2,-3 are time lags).

TOTRXFIRES Total prescribed fires within the San Jacinto Ranger District for a +

particular year (-1,-2,-3 are time lags).

DirtDist The distance to the nearest dirt road, in meters from a central No expectation

location of each hunting zone.

TrailDist The distance to the nearest trail, in meters from a central location No expectation

of each hunting zone.

Fire_Dist The average distance from a central location of each hunting No expectation

zone to the central point of a wildfire.

HuntArea The size of each harvest area, measured in acres. +

Oct_Temp The average temperature in October, degrees Fahrenheit. _

Year A trend variable to look for systematic changes. No Expectation

Estimated Production Functions

Macro Time Series San Jacinto Ranger District Equations

Allowing for non-linearity proved to be a better predictor of deer harvest

than the linear models (linear results available from the authors) so we present

the double log model. Results from preliminary regressions also suggested

combining the wildfire and prescribed burn into one variable. In table 2, the

coefficient for total fire has a small magnitude of .048, but it has a significant

t-statistic of 2.3. The sign on this variable is positive and the coefficient can be

interpreted as an elasticity due to the log-log functional form. Therefore, a 1%

increase in acres burned will lead to a .048% increase in deer harvest. The

Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat Loomis, González-Cabán, Griffin, and Wu
192 USDA Forest Service Proceedings RMRS-P-29. 2003.

other significant variables are October temperature and year. The statistically

significant negative sign on the October temperature coefficient is consistent

with the opinion of hunters that an increase in temperature results in a decrease in the number of deer harvested. The year variable indicates that a

systematic time trend effect exists within the model. This model’s explanatory

power is reasonably good with an R2 value of .67. The Durbin-Watson statistic of 2.06 indicates that autocorrelation is not a problem. The same model

presented in table 2 was estimated with a 1-year lag but this model did not

perform well using the lag. The coefficient on the lag of total fire (-1) was .01

and the t-statistic is .44, which indicates the lag is insignificant. The R2

 value

did not change from the model with the no lag (results available from

authors).

Summary of Micro Regressions Based on GIS Analysis

The two regression models estimated using GIS derived data are presented

in this section: one count data and the other OLS, both of which show prescribed burning had a statistically significant effect on deer harvest. As can be

seen in table 3a, total acres of prescribed fire is significant during the year of

the prescribed fire, and its significance declines over the next three years in the

count data model. During the first year, prescribed fire’s coefficient is .044

with a t-statistic of 2.4. Since this count data model logs the fire acreage variables it is equivalent to a log-log model. As such, the .044 is the elasticity,

which is remarkably similar to the .048 elasticity in the macro time-series model

reported in table 2. The total acres of wildfire variable was not significant for

any of the years in this equation. The total area count data model has an R2

value of .25.

Using OLS as an estimator of deer harvest per acre as a function of fire and

the other variables provides a similar pattern of signs and significance as the

total area count data equation. In this model, a double log form was also used,

but this time the dependent variable acts as a controlling measure for the size

of each harvest area by dividing harvest in each hunting zone by the number

of acres in each zone. The result of this model in table 3b shows that prescribed burning has a statistically significant effect on deer harvest in the first

year with a t-statistic of 2.25. Then during the years following the fire, prescribed burning becomes less significant, which corresponds to the previous

count data model. The only time wildfire has a significant impact is during

the second year following the burn. The sign of the coefficient for wildfire in

the second year is negative and less than one, which would imply a negative

effect on deer harvest in that year. Distance to dirt roads is also significant,

Table 2—Macro time series ranger district log-log model. Dependent variable is the

log of SJRD deer harvest.

Variable Coefficient Std. error t-statistic Probability

Constant 41.80865 11.07 3.776 0.002

ln_Totalfire 0.048735 0.0205 2.371 0.032

Total_Precip -0.00096 0.0026 -0.366 0.719

Oct_Temp -0.02703 0.0106 -2.536 0.023

Year -0.01785 0.0055 -3.199 0.006

R-squared: 0.677 Mean dependent var: 4.808

Adj. R-squared: 0.584 S.D. dependent var: 0.202

S.E. of regression: 0.1303 F-statistic: 6.343

Durbin-Watson: 2.066 Prob (F-statistic): 0.002

Loomis, González-Cabán, Griffin, and Wu Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat
USDA Forest Service Proceedings RMRS-P-29. 2003. 193

a t-statistic of 5.17 and a negative coefficient of -.012. This means that harvest

areas farther away from dirt roads have a lower probability of harvesting a

deer. The positive sign on the distance to trails variable implies increases in the

probability of a deer harvest the farther the hunting area is from a trail. All the

other variables in this model fail to be significant indicators of deer harvest,

Table 3b—Least squares deer harvest per acre using GIS data model with lags. Dependent

variable is LDEERKILLAC; n=825. Method is least squares.

Variable Coefficient Std. error t-statistic Probability

C 1.341834 1.58826 0.844 0.3984

LAVG_ELEV -0.009703 0.00927 -1.046 0.2958

LTOTWFIRES 0.001163 0.00120 0.963 0.3357

LTOTWFIRES(-1) 0.000499 0.00116 0.429 0.6678

LTOTWFIRES(-2) -0.002231 0.00107 -2.086 0.0373

LTOTWFIRES(-3) -0.001775 0.00109 -1.627 0.104

LTOTRXFIRES 0.002635 0.00116 2.254 0.0244

LTOTRXFIRES(-1) 0.002134 0.00188 1.134 0.2568

LTOTRXFIRES(-2) 0.001333 0.00154 0.862 0.3889

LTOTRXFIRES(-3) 0.001212 0.00130 0.926 0.3546

LDIRTDIST -0.012952 0.00250 -5.174 0

LTRAILDIST 0.01825 0.00222 8.213 0

LFIRE_DIST 0.005144 0.00320 1.607 0.1084

LHUNTAREA -0.008678 0.00615 -1.409 0.159

LOCT_TEMP -0.050413 0.08604 -0.585 0.5581

YEAR -0.002827 0.00082 -3.436 0.0006

R2

: 0.138 Mean dependent var: -4.532

Adjusted R2

: 0.122 S.D. dependent var: 0.093

S.E. of regression: 0.087 F-statistic: 8.684

Prob (F-statistic): .0000

Table 3a—Count data model based on GIS using total acres burned with lags. Dependent

variable is DEERKILL; n = 825. Method is ML - Negative Binomial Count.

Coefficient Std. error t-statistic Probability

C 62.96425 23.1157 2.7238 0.0065

LAVG_ELEV -0.237276 0.13070 -1.8153 0.0695

LTOTWFIRES 0.010712 0.01714 0.6248 0.5321

LTOTWFIRES(-1) 0.008299 0.01701 0.4877 0.6257

LTOTWFIRES(-2) -0.027728 0.01548 -1.7902 0.0734

LTOTWFIRES(-3) -0.02466 0.01557 -1.5829 0.1134

LTOTRXFIRES 0.044067 0.01790 2.4608 0.0139

LTOTRXFIRES(-1) 0.027531 0.02701 1.0192 0.3081

LTOTRXFIRES(-2) 0.011491 0.02223 0.5169 0.6052

LTOTRXFIRES(-3) 0.011491 0.01866 0.6155 0.5382

LDIRTDIST -0.233799 0.03774 -6.1943 0

LTRAILDIST 0.395161 0.04175 9.4633 0

LFIRE_DIST 0.072684 0.04739 1.5335 0.1251

LHUNTAREA 0.940678 0.08699 10.812 0

OCT_TEMP -0.012073 0.01681 -0.7178 0.4728

YEAR -0.034733 0.01176 -2.9534 0.0031

Overdispersion parameter:

Alpha: C(17) -0.2810 0.1081 -2.598 0.0094

R2

: 0.257 Mean dependent var: 1.7587

Adjusted R2

: 0.242 S.D. dependent var: 2.6113

S.E. of regression: 2.2731 Restr. log likelihood: -1920.6

LR index (Pseudo-R2): 0.3051

Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat Loomis, González-Cabán, Griffin, and Wu
194 USDA Forest Service Proceedings RMRS-P-29. 2003.

except for the trend variable, year. Therefore, some unidentifiable systematic

temporal change is occurring within the model. Overall this model has a lower

level of explanatory power than the total area micro count data model. The R2

value using OLS is .13 as compared to twice this level of explanatory power in

the total area count data model.

Applying the Regression Production Functions

To calculate the incremental effects of different levels of prescribed burning

on deer harvest, the acres burned variable is increased from one level to a

higher level in the regression model. We use the double-log macro time-series

model and the micro GIS-based double-log total area count data models, as

these two models have the highest explanatory power. The resulting predicted

change in deer harvest will be valued in dollar terms in the next section.

Applying Results of Micro GIS Production Function Model

The results of the “Total Acres Burned” count data model from table 3a

provide positive evidence on the desirable effects of prescribed burning programs on deer harvest. The first row in table 4 forecasts the estimated number

of deer that would be harvested without having a prescribed burning

program. The second row in table 4 represents the current level of prescribed

burning. The effect of increasing prescribed burning is calculated by increasing the number of acres burned in each of the 37 hunting areas by 100 acres

per hunting area, and then 200 acres per hunting area (for a total of 8,510

acres) to evaluate a wide range of prescribed burning levels in the SJRD. The

first level (1,100 acres) is about the average prescribed burning over the last

20 years. Maintaining this level of prescribed burning does provide a significant increase in deer harvest over the no burning level. However, the gain in

deer harvest increases more slowly with additional increases in burning in each

hunt area.

Table 4—Comparison of deer harvest response to prescribed burning using the macro time series model

and GIS micro model.

Macro time Time series GIS micro GIS marginal

RX acres Additional series model: marginal increase model: # deer increase in

burned acres burned # deer harvested in deer harvest harvested deer harvest

1 NA 83 NA 42 NA

1110 1110 116 33 58 16

4810 3700 124 8 66 8

8510 3700 128 4 71 5

Applying Results of Macro Time Series Production Function

Model

To estimate the change in deer harvest using the Macro Time Series

Production Function Model, the double log model reported in table 2 is used.

The total fire variable in this model is increased and the predicted level of deer

harvest is calculated at the mean of the other variables. This is done at the

same four acreage levels used above.

Loomis, González-Cabán, Griffin, and Wu Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat
USDA Forest Service Proceedings RMRS-P-29. 2003. 195

The results in table 4 suggest there is a substantial gain in deer harvest with

the first 1,100 acres burned, especially as calculated from the macro timeseries model. However, a very similar diminishing marginal effect is evident

from both the macro time-series production function regression and the

micro GIS production function regression after burning more than 1,100

acres. That is, regardless of the spatial level of detail adopted, burning an

additional 3,700 acres is expected to result in about eight more deer being

harvested in the SJRD.

In order to determine the economic efficiency of additional prescribed burning it is necessary to compare the benefits of additional prescribed burning in

the form of the economic value of deer harvest against the costs. It is to the

development of the valuation data that we now turn.

Valuation of Deer Hunting

According to CDFG, deer hunting is considered to be one of the major

outdoor recreation activities in SJRD (Gibbs et al. 1995). Previous research

on deer hunting in California showed that increased success rates and opportunities to harvest a trophy deer increase the economic value of deer hunting

(Loomis et al. 1989, Creel and Loomis 1992).

Travel Cost Method for Valuation of Deer Hunting

The Travel Cost Method (TCM) has been a primary approach for valuing

recreational hunting. The basic concept of TCM is that the travel cost (i.e.,

transportation cost, travel time) to the site is used as a proxy for the price of

access to the site. When hunters are surveyed and asked questions about the

number of trips they take and their travel cost to the site, enough information

is available to estimate a demand curve. From the demand curve, net willingness to pay or consumer surplus can be calculated (Loomis and Walsh 1997).

Besides variable travel cost or its proxy, travel distance, inclusion of a travel

time variable in the demand function is necessary to represent the opportunity

costs of time as part of travel costs. Cesario (1976) suggested one-fourth the

wage rate as an appropriate estimate of the opportunity cost of time based on

commuting studies. For individuals with fixed workweeks, recreation takes

place on weekends or during pre-designated annual vacation and cannot be

traded for leisure at the margin. In such cases, Bockstael et al. (1987) and

Shaw (1992) suggest that the opportunity cost of time no longer need be

related to the wage rate. These studies suggest that both the travel cost and

travel time be included as separate variables, along with their respective constraints, income and total time available for recreation.

Table 5 contains a list and definition of variables used in the TCM demand

model. For this study we chose the TCM variables according to the consumer

demand theory and existing literature on deer hunting in California. Individuals who hunt on opening day, belong to hunting organizations, hunted in

previous seasons, and had a successful deer harvest may take potentially more

hunting trips because such hunters have higher preferences, experience or skill

in deer hunting recreation. Because a majority of hunters in our dataset work

a fixed workweek, we assume deer hunters maximize utility level subject to

their income and time constraints (Shaw 1992). In other words, total time

available for recreation is a constraint similar to income for time intensive

activities like hunting. The total time budget is constructed for the TCM

model using responses to the survey questions regarding availability of

Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat Loomis, González-Cabán, Griffin, and Wu
196 USDA Forest Service Proceedings RMRS-P-29. 2003.

vacation time and time periods chosen to hunt (e.g., weekends only versus

weekdays). In this study, the total time budget ranged from 8 to 31 days

because the deer-hunting season in SJRD lasted for one month only.

Count Data Nature of TCM Dependent Variable

The nonnegative integer characteristic for the dependent variable, number

of seasonal trips, is from a count data process. Given the count data form of

the dependent variable, a preferred estimation technique would be the negative binomial count model to estimate the demand function (Creel and Loomis

1990). The negative binomial is the more generalized form of the Poisson

distribution, which allows the mean of trips to be different from its variance.

The count data TCM model is specified in equation 2.2:

NUMTRIPS = EXP (C(1) + C(2)*AGE + C(3)*DEERKILL

+ C(4)*HUNTOPEN + C(5)*HUNTORG + C(6)*PREVSEAS

+ C(7)*PRIVLAND - C(8) * RTRAVMILES + C(9)* PCINC

+ C(10)*TOTIMEBUD - C(11)*TRAVTIME) [2.2]

In equation 2.2, we expected the coefficient for DEERKILL (i.e., C (3)) to

have a positive sign, because hunters would likely take more hunting trips if

the hunting quality has been good. Also, if hunters hunt on the opening day

(i.e., C (4)), private land (i.e., C (7)), and/or previous seasons (i.e., C (6)),

and belong to hunting organizations (i.e., C (5)), then we expected a positive

effect on the number of trips the hunter takes as these variables indicate a

strong preference for the deer hunting activity. For those hunters with a higher

income level (i.e., C (9)) and/or higher total time budget (i.e., C (10)) we

expect more hunting trips as well due to less binding income and time constraints. However, round-trip travel distance (i.e., C (8)) and travel time (i.e.,

C (11)) are expected to have negative effects on the number of hunting trips

because increases in these two variables result in higher hunter’s expense.

Table 5—Variables included in travel cost model.

Variable Definition

Dependent variable

NUMTRIPS Number of primary purpose of deer hunting trips taken to the SJRD

during 1999 deer hunting season.

Independent variables

AGE Hunter’s age

DEERKILL Did you harvest a deer in this area during this hunting season?

1= YES, 0 = NO

HUNTOPEN Did you hunt on opening day of the season?

1= YES, 0 = NO

HUNTORG Are you a member of a sportsman’s organization?

1= YES, 0 = NO

PREVSEAS Have you hunted in this area in a previous season?

1= YES, 0 = NO

PRIVLAND Did you hunt on private land?

1= YES, 0 = NO

RTRAVMILES Round trip travel miles from home to the hunt zone

PCINC Hunter income

TOTIMEBUD Total time budget during hunting season

TRAVETIME Number of hours one-way travel time

Loomis, González-Cabán, Griffin, and Wu Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat
USDA Forest Service Proceedings RMRS-P-29. 2003. 197

Calculation of Consumer Surplus in TCM

The consumer surplus from deer hunting is computed from the demand

curve as the difference between what people are willing to pay (e.g., the entire

area under the demand curve) and what people actually pay (e.g., their travel

costs). Because the count data model is equivalent to a semi-log functional

form, consumer surplus from a trip is calculated as the reciprocal of the coefficient on round trip travel miles, expressed in RTRAVMILES scaled to dollars using the cost per mile (Creel and Loomis 1990).

Hunter Survey Data

For cost effectiveness in data collection, a mail questionnaire was sent to a

random sample of deer hunters with licenses for zone D19, which includes

the San Jacinto Ranger District. Of 762 questionnaires mailed to deer hunters

in California during the 1999 hunting season, 7 were undeliverable. A total of

356 deer hunters’ responses were collected after two mailings. The response

rate is approximately 47%. Among these respondents, 69 did not hunt in the

San Jacinto Ranger District portion of Zone D19. The response rate of this

study is suspected to be low because many of the hunters that did not hunt in

the SJRD portion of the D19 Hunt Zone may not have returned the survey.

Statistical Results

Estimation results are summarized in table 6. There is a negative effect of

travel miles, travel time, and income on number of trips taken. Income, in this

study, is insignificant. The regression results of this study indicate that hunters

which successfully harvested a deer during the hunting season (i.e.,

DEERKILL), hunted on opening day (i.e., HUNTOPEN), hunted in this

area in a previous season (i.e., PREVSEAS), and had a larger total time budget

(i.e., TOTIMEBUD) had positive and significant effects on the number of

hunting trips taken. Consistent with economic theory, hunters with longer

round trip travel miles (RTRAVMILES) and greater travel time (TRAVTIME)

tend to take fewer hunting trips.

Table 6—Estimated negative binomial count data TCM demand equation. Dependent variable

is NUMTRIPS.

Coefficient Std. error Z-stats Probability

Constant 1.324485 0.2163 6.1226 0.0000

AGE 0.001395 0.0037 0.3684 0.7125

DEERKILL 0.366571 0.1547 2.3695 0.0178

HUNTOPEN 0.524153 0.1148 4.5640 0.0000

HUNTORG 0.067655 0.1058 0.6390 0.5228

PREVSEAS 0.285282 0.1344 2.1217 0.0339

PRIVLAND 0.038041 0.1314 0.2892 0.7724

RTRAVMILES -0.002230 0.0008 -2.4906 0.0128

PCINC -1.00E-06 2.78E-06 -0.359 0.7192

TOTIMEBUD 0.010128 0.0048 2.0994 0.0358

TRAVTIME -0.289315 0.0867 -3.3340 .0009

R2

: 0.2058, Adjusted R2 : 0.1685

Consumer surplus: $134.53/trip

90% confidence interval: $81.13 ~ 393.59

Marginal consumer surplus per deer harvested: $257.17/deer

90% confidence interval: $154 ~ 752

Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat Loomis, González-Cabán, Griffin, and Wu
198 USDA Forest Service Proceedings RMRS-P-29. 2003.

In table 6 the consumer surplus is calculated by:

1/ b (i.e., coefficient of distance) * $0.3/mile (i.e., cost per mile).

= 1/0.002230 *$0.3 = 448.43 * $0.3 = $134.53/trip, where the $0.30 is the

sample average cost per mile.

Finally, the 90% confidence interval in table 6 is obtained by the following

equation:

90% confidence interval on consumer surplus per trip = 1/ ( b DIST ± 1.64

* 0.000895) * $0.30/mile = $81.13 ~ $393.59 dollars per trip

Estimating the Benefits of Harvesting an Additional Deer

The average number of trips per hunter is 5.56 trips and 10% of deer hunters successfully harvested a deer. To calculate the incremental or marginal

value of an additional deer harvest we can use the TCM demand equation to

predict the extra number of trips deer hunters would take if they knew they

would harvest a deer that season. This essentially shifts the demand curve out

by the amount of the coefficient on deer harvest. The equation predicts that

each hunter would take 1.9116 more trips each season if they knew they would

harvest a deer. Therefore, the marginal value of another deer harvested (i.e.,

marginal consumer surplus) is equal to $134.53 * 1.9116 = $257.17 per deer

harvested. Finally, the 90% confidence interval in table 6 for an additional

deer harvested is obtained by applying the 90% CI on the value per trip times

the additional number of trips taken by the hunter: 90% confidence interval of

the value of harvesting an additional deer =

1.9116 * $81.13 ~ 1.9116*393.59 = $155 ~ $752 dollars per deer harvested.

Benefits of Prescribed Burning

Table 7 summarizes this study’s main conclusion—the annual deer hunting

benefits of additional acres of prescribed burning. While the initial deer hunting benefit response to prescribed burning of 1,100 acres ranges from $4,112

to $8,481 depending on the model used, the incremental gains for more than

the current acreage of prescribed burning is quite similar across models. That

is, the annual economic hunting benefits of increasing prescribed burning

from its current level of 1,110 acres to 4,810 acres is $2,056, regardless of the

model used. Likewise for an additional 3,700 acres of prescribed burning to

Table 7—Annual deer hunting benefits from increased prescribed burning: macro time

series model and GIS micro model results.

Annual

Time series increase GIS GIS Annual

Additional marginal in deer marginal increase in

RX acres acres increase in hunting increase in deer hunting

burned burned deer harvest benefits deer harvest benefits

1 NA NA NA NA NA

1110 1110 33 $8,481 16 $4,112

4810 3700 8 $2,056 8 $2,056

8510 3700 4 $1,028 5 $1,285

Loomis, González-Cabán, Griffin, and Wu Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat
USDA Forest Service Proceedings RMRS-P-29. 2003. 199

8,510 acres, the deer hunting benefits are calculated to be between $1,028 to

$1,285 each year, fairly similar despite the different modeling approaches.

Comparison to Costs

Discussions with fire management personnel on the San Bernadino

National Forest suggested that their prescribed burning costs range from $210

to $240 per acre. This is a lower total cost per acre than reported by GonzálezCabán and McKetta (1986), but substantially higher than the direct costs per

acre for southwestern National Forests in Wood (1988). Nonetheless, if we

use the $210 per acre figure, the full incremental costs of burning the first

1,100 acres would be $231,000, with each additional 3,700 acres burned

costing $779,100. The additional benefits of deer hunting benefits represent

at most about 3.4% of the total costs of performing the first 1,100 acres of

prescribed burning. This finding can be used in two ways. First, the incremental costs of including deer objectives in the prescribed burn should not exceed

$8,000, as the incremental benefits are no larger than this. Second, the other

multiple use benefits such as watershed and recreation, as well as the hazard

fuel reduction benefits to adjacent communities, would need to make up the

difference if the prescribed burning program is to pass a benefit-cost test.

Conclusion

This study evaluated the response of deer harvest and deer hunting benefits

to prescribed burning in the San Jacinto Ranger District in Southern California. To estimate hunter’s benefits or willingness to pay (WTP) for harvesting

an additional deer, the individual observation Travel Cost Method was used

resulting in a mean WTP to harvest another deer of $257. With regard to the

response of deer harvest to prescribed and wildfire, we compared a macro

level, time-series model which treated the entire San Jacinto Ranger District

as one area and a micro GIS model which disaggregated the Ranger District

into the 37 hunting areas delineated by California Department of Fish and

Game. The macro time-series model estimated a larger response to burning of

the first 1,000 acres than the micro GIS model did, but for increases in fire

beyond 1,000 acres, the two models provide nearly identical estimates.

Using the marginal willingness to pay for harvesting another deer calculated from the TCM demand model, the deer harvest response to fire yields

annual economic benefits ranging from $4,112 to $8,481 for the first 1,100

acres burned. For an additional 3,7000 acres burned, the gain is $2,056 annually, while for a second increase of 3,700 acres (for a total of 8,510) the

increase ranges from $1,028 to $1,285 per year. The costs of prescribed burning on the San Bernadino National Forest range from $210 to $240 per acre.

Thus the cost to burn an additional 1,100 acres is $231,000, which is an

order of magnitude larger than the deer hunting benefits gained. Specifically,

the deer hunting benefits of the first 1,100 acres burned represent about 3.4%

of the total costs of conducting the first 1,100 acres of prescribed burning.

However, there are probably other multiple use benefits such as protecting

watersheds and wildfire hazard reduction. These other multiple use benefits

of prescribed burning would have to cover the rest of the costs of prescribed

burning if the program is to be economically feasible. Investigating the extent

of these benefits would be a logical next step in evaluating the economic efficiency of prescribed burning in the San Jacinto Ranger District.

Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat Loomis, González-Cabán, Griffin, and Wu
200 USDA Forest Service Proceedings RMRS-P-29. 2003.

While fire management practices have been identified as having widespread

impacts on deer habitats, many other factors that affect deer habitat exist.

These other factors include livestock grazing, timber harvesting, urban

development, diseases, habitat loss, and annual weather patterns (CDFG, 1998).

This study attempted to take into account as many factors as possible, but the

amount of data and time available for modeling were a constraint.

Some future improvements in our modeling effort that may better isolate

the effects of prescribed burning on deer habitat include controlling for the

severity of wildfire as different fire severities will have different effects on vegetation and soils. Furthermore, including vegetation and soils layers in the

GIS model, rather than using elevation as a proxy, could improve the predictive ability of the GIS-based model.

Subject to these caveats, we have demonstrated two approaches to estimate

a production function relating prescribed burning to effects on deer harvest.

We found positive and significant effects on deer harvest for the two GIS

models and a positive impact of fire using a macro-time series model. The

USDA Forest Service and California Department of Fish and Game can make

use of these approaches for future cost-benefit analysis of prescribed burning.

Acknowledgments

We wish to thank Dan McCollum and Diane Freeman of the USDA Forest

Service and Jane McKeever of the California Department of Fish and Game

for assistance in the study design and data collection. We would also like to

thank Lucas Bair of Colorado State University for research assistance regarding the cost of prescribed burning and editorial assistance. We would also like

to thank two anonymous reviewers for their suggestions that improved the

exposition of this paper.

References

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the recreation demand model. American Journal of Agricultural Economics 69(2):

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California Department of Fish and Game. 1998. An assessment of mule and blacktailed deer habitats and populations in California, with special emphasis on public

lands administered by the Bureau of Land Management and the United States

Forest Service. General Technical Report. 89p.

Cesario, F.J. 1976. Value of time in recreation benefit studies. Land Economics 52(1):

32-41.

Creel, M. D. and J. Loomis 1990. Theoretical and empirical advantages of truncated

count data estimators for analysis of deer hunting in California. American

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Creel, M. D. and Loomis, J. 1992. Modeling hunting demand in the presence of a

bag limit, with tests of alternative specifications. Journal of Environmental

Economics and Management 22(2): 99-113.

Gibbs, P., D. Freeman, C. Harvey, R. Lawson, K. Valenzuela, D. Pumphrey. 1995.

Area analysis and plan for prescribed burning. San Jacinto Ranger District, San

Bernadino National Forest. 23p.

Loomis, González-Cabán, Griffin, and Wu Linking GIS and Recreation Demand Models to Estimate the Economic Value of Using Fire to Improve Deer Habitat
USDA Forest Service Proceedings RMRS-P-29. 2003. 201

González-Cabán, A. and C. McKetta. 1986. Analyzing fuel treatment costs. Western

Journal of Applied Forestry. 1(4): 116-121.

Hesseln, Hayley. 2000. The economics of prescribed burning: A research review.

Forest Science 46(3): 322-334.

Kie, John G. 1984. Deer habitat use after prescribed burning in Northern California.

Research Note PSW-369. Berkeley, CA: U.S. Department Agriculture, Forest

Service, Pacific Southwest Forest and Range Experiment Station. 3p.

Klinger, Robert C.; Kutilek, Michael J.; Shellhammer, Howard S. Population responses

of black-tailed deer to prescribed burning. Journal of Wildlife Management 53(4):

863-870.

Loomis, J., D. Updike and C. Unkel 1989. Consumptive and nonconsumptive values

of a game animal: The case of California deer. Transactions of the 54th North

American Wildlife and Natural Resources Conference. Wildlife Management

Institute, Washington DC.

Loomis, John and Walsh, Richard G. 1997. Recreation economic decision: comparing

benefits and costs 2nd Ed. State College, PA: Venture Publishing, Inc. 430 p.

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202 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 203

Co-Firing Wood Biomass With Coal at the

Cañon City Power Plant

Daniel Prokupets1, Dr. Kurt Mackes2, and Dr. Skip Smith2

Abstract—Green full tree chips produced from small diameter trees can be co-fired

with coal. This paper reports some of the challenges and benefits at a Cañon City

power plant. Tree chips include the main stem, branches, tops, needles, and bark. If

a sufficient supply of low-cost wood is available, the power plant will continue to

burn it. Collaboration among agencies and private companies is crucial for continued success.

Introduction

Past fire management and suppression practices have created conditions

favorable for catastrophic fire throughout wildland/urban interface

zones of the Intermountain West. These past practices are responsible for greater

standing fuel loads that potentially threaten public safety, watershed productivity, and forest health. The National Interagency Fire Center (2002)

reported that over 8.4 million acres of forest land burned nationwide during

the 2000 fire season, which compares to a 10-year average (1990-1999) of

about 3.8 million acres per year. Unfortunately, unless efforts to reduce standing

fuel loads are implemented, it appears that conditions will continue to be

favorable for large wildfires in future years.

In response to the 2000 fire season, the National Fire Plan (Department of

the Interior 2001) was developed. One objective of the National Fire Plan is

to implement strategies that mitigate the severe fire conditions existing on

forest land throughout the Intermountain West. These strategies revolve primarily around forest restoration (fire mitigation) thinning, prescribed burning, or a combination of both (usually mechanical removal or thinning followed

by prescribed burning). Because of inherent risks associated with prescribed

burns in the absence of prior fuel reduction, forest restoration thinning is now

being utilized throughout the Intermountain West to mitigate fire conditions.

Forest restoration thinning involves removing primarily small diameter trees,

which are found abundantly in the Intermountain West. In Colorado, small

diameter trees are considered to be less than 12 inches in diameter at breast

height (dbh). However, the majority of trees removed during thinning projects

are usually considerably less than 12 inches dbh and often less than 5 inches

dbh. In addition to being small, these trees tend to have many limbs and

correspondingly their wood has many knots usually considered to be defects

in solid wood products. Small diameter trees also have disproportionately high

quantities of juvenile and reaction wood that further reduce wood quality. As

a result, many of these trees are currently unmerchantable.

Therefore, the question has arisen, how can these small trees be utilized?

The potential for producing composite products, such as oriented strand board

(OSB) or particleboard, and pulp and paper products is limited in Colorado

1 Colorado State Forest Service.

2Department of Forest, Rangeland, and

Watershed Stewardship, Colorado State

University, Fort Collins, CO.
204 USDA Forest Service Proceedings RMRS-P-29. 2003.

by the lack of infrastructure. This is also true for other products such as pelletized fuel. Because of the high capital investment required to build plants for

producing these products, it is doubtful that a private company would risk

building such a plant unless guaranteed a wood supply. No such guarantees

currently exist. Because small trees have little or no value, much of this material is left in the forest lopped and scattered or chipped and blown back onto

the forest floor. In addition to being wasteful, the impacts of doing this on soil

and ground water are unclear and currently being studied.

Utilizing green wood chips produced from small diameter trees to generate

electric power is one alternative. There are currently numerous plants throughout the country that utilize wood biomass as a source of fuel for generating

electricity (Bain and Overend 2002). Although there are plants that use only

biomass, many also co-fire wood with other fuels such as coal or natural gas.

Co-firing is not a “new” technology and many power plants that operate on coal

do not even have to modify their handling and boiler systems to utilize some

wood. Therefore, these plants can burn wood with little or no capital investment.

Background

The W. N. Clark power plant located in Cañon City, Colorado, is capable of

co-firing wood with coal. The plant is an older facility and has experimented

with co-firing in the past. Prior efforts were discontinued because of inconsistent wood supply and quality issues with the size of wood particles provided.

Wood residues procured from a local sawmill were not of uniform size, which

caused handling problems. Also, fine wood particles and dust in the residues

had a tendency to become airborne and were considered a potential explosion

risk. Nonetheless, the plant expressed interest in co-firing if a sufficient economical wood supply of acceptable quality and size could be procured.

This research was conducted to demonstrate that the W. N. Clark power

plant could utilize green full tree chips produced from small diameter trees.

Full tree chips included the main stem, branches, tops, needles, and bark of

the tree. A resource assessment is also in progress to identify potential sources

of low cost wood and to determine the best scenario for wood delivery, processing, and storage. This project was a collaborative effort of Aquila (W. N.

Clark power plant), Colorado State Forest Service, Department of Forest Sciences at Colorado State University, City of Cañon City, Fremont County, and

Sangre de Cristo Resource Conservation & Development.

Discussion

The W. N. Clark power plant is currently permitted to burn up to 5 percent

wood by weight with coal. To demonstrate that the plant could burn green

full tree chips for an extended period of time, ponderosa pine chips were supplied from forest restoration thinning projects in the region. The Colorado

State Forest Service subsidized the transportation of chips to the plant. Chip

deliveries began in September 2001 and the plant began co-firing immediately.

Initially a mix of less than 1 percent green wood chips by weight or 1 to 2 tons

of wood per day was used. Over 200 tons of green wood chips have been cofired with coal since September.

Chip moisture content ranged from 20 to 70 percent. The moisture in the

chips did not present any significant problems for plant systems except for

Prokupets, Mackes, and Smith Co-Firing Wood Biomass With Coal at the Cañon City Power Plant
USDA Forest Service Proceedings RMRS-P-29. 2003. 205

reducing the amount of recoverable heat. Because of the impact on recoverable heat, wood chips used in this research would have had to be considerably

drier (20 percent moisture content or less) to significantly increase the amount

used. The plant is currently evaluating ways to increase the percentage of wood

burned and there is potential to burn up to 25 tons per day.

Several sources of wood for the plant are being investigated. These include

continued supply of small diameter trees from forest restoration thinning

projects in the region, local primary and secondary wood processors, and arborists and municipalities in the area.

As part of the National Fire Plan, 38,400 acres are to be treated in

Colorado during 2002 (Department of the Interior 2001). Based on an analysis

by Lynch (2000) of forest restoration thinning work done at Cheesman

Reservoir in Colorado, treating these acres could conservatively yield from 9

to 15 green tons of green biomass per acre. Therefore, 345,000-576,000 tons

of green biomass could potentially be available. The primary constraint on

availability is the distance that this biomass would need to be transported.

Transportation of green wood chips to the W. N. Clark power plant is

currently being subsidized and even though the plant can pay for wood, in the

absence of the subsidy, it is not likely that chip transport would be economical. Transportation data is currently being collected and maximum haul

distances are not yet known, but they are considerably less than 100 miles

(more likely in the range of 20 to 30 miles). In addition to acres that are to be

treated under the National Fire Plan, more wood biomass could be available

through road construction, defensible space efforts, and other logging activities not considered as part of the National Fire Plan.

Primary and secondary wood processors could also be a significant supplier

of wood chips. In spring 2001, Ward (2000) surveyed 173 primary and

secondary wood manufacturers operating in Colorado. The 75 companies that

responded generated 380 tons of residues per week on average. About

83 percent were willing to consider alternatives to current wood biomass disposal practices. More importantly, two sawmills in the immediate area said

they would be interested in supplying residue to the plant. However, handling

and dust problems that occurred when the W. N. Clark power plant attempted

to use sawmill residues in the past would likely reoccur.

Another possible supply of green wood chips could be urban wood residues. This would include residues from work done by local arborists and municipalities, as well as construction and demolition debris going into the local

landfill. An advantage of this wood material is that it can often be procured at

little or no cost. Results from a survey of local arborists conducted by Prokupets

(2002) revealed that they conservatively generate an estimated 2000 tons of

wood debris annually, which is currently enough to supply the W. N. Clark

power plant. However, a preliminary test burn conducted with wood residues

(chips) supplied by a local arborist was not successful. As with wood processing residues, wood particle size was inconsistent and over sized (long, stringy)

particles clogged the fuel handling system at the plant. As a result, future

research will include evaluating wood processing equipment (chippers and

grinders) to determine the most cost effective way of producing a wood chip

suitable for use at the plant.

Conclusion

Research to date has demonstrated that green full tree chips can be co-fired

with coal at the W. N. Clark power plant. The plant has indicated that it will

Co-Firing Wood Biomass With Coal at the Cañon City Power Plant Prokupets, Mackes, and Smith
206 USDA Forest Service Proceedings RMRS-P-29. 2003.

continue to burn wood if a sufficient supply of adequate low cost wood is

available. Although it appears that there is a sufficient supply of low cost wood

available to the plant, further research is necessary to determine how best to

collect and process the wood biomass into a useful size and form.

Generally, project success depends on overcoming several major challenges.

The costs associated with procuring and co-firing wood chips with coal must

be economical. The logistics of wood delivery, chipping, and storage must be

evaluated to determine the most cost effective methods. Transportation is a

major cost of getting wood from forest restoration thinning projects to the

plant. Haul distances will likely have to be considerably less than 100 miles

and government subsidies may be necessary to cover all transportation costs.

Perhaps most crucial to the success of this project is the collaboration of the

various agencies, private companies, and individuals who will be involved.

Literature Cited

Bain, Richard L., and Ralph P. Overend. 2002. Biomass for Heat and Power. Forest

Products Journal. 52(2):12-19.

Department of the Interior. 2001. National Fire Plan Website: Colorado State

Summary from Department of the Interior. 2001. <http://www.fireplan.gov/

statebystate/Colorado1.cfm>

Lynch, Dennis L. 2000. Summary Report: Forest Restoration Thinning Project on

Denver Water Board Property at Cheesman Reservoir and Trumbull Sites, May 8

to July 2000. Colorado State University. Fort Collins, CO.

National Interagency Fire Center. 2002. Wildland Fire Season 2000 at a Glance.

NIFC, Boise, Idaho. <http://www.nifc.gov/fireinfo/2000/>

Prokupets, Dan. 2002. Phone Survey of Arborists in the Cañon City Area. Colorado

State Forest Service, Fort Collins, CO.

Ward, Julie E. 2000. Phone Survey of Primary and Secondary Wood Processors along

the Front Range of Colorado. Colorado State Forest Service, Fort Collins, CO.

Prokupets, Mackes, and Smith Co-Firing Wood Biomass With Coal at the Cañon City Power Plant
USDA Forest Service Proceedings RMRS-P-29. 2003. 207

Treatment—Social Issues
208 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 209

Fire Social Science Research: Opening Remarks

Antony S. Cheng1

Introduction

Determining the “appropriate place, appropriate time” for fuels treatments in response to forest wildfire risk is a social process that blends

scientific information with social values and attitudes. Natural resource scientists and managers must realize that whether they like it or not, they contribute

to this process. Social science perspectives and research can complement technical analyses to determine if, when, and where fuel treatments are appropriate.

Why Bother With Social Science?

The forestry community has long been aware of the importance of garnering public support for management decisions. Public land management

decisions in particular have been subject to scrutiny and conflicting perspectives. Indeed, opinion polls and more rigorous random sample surveys show

that the public is wary of logging on public lands. In recent years, this wariness has transferred even to fuel treatments to reduce the risk of catastrophic

wildland forest fires. Even in geographic areas such as the Intermountain West

where large forest fires have raised public awareness about unhealthy forest conditions, there are mixed messages about the relationship between the role of fire,

forest health conditions, and forest practices. Fuel treatments such as thinning are

still relatively new concepts and may conflict with deeply held values.

Perhaps more significant is the varying degrees of trust in resource managers. For whatever reason, public distrust of forestry professionals can be the

most significant barrier to implementation, even if the science and economics

are sound. For the public’s part, there is a lot at stake—aesthetics, property

values, and conflicting visions of what forests should look like in the future.

In short, the sustainability of fuel treatment programs turns on public understanding and acceptance. Social science methods can provide insights and

methodologies to identify gaps in public understanding and barriers to acceptance. Social science can also provide useful perspectives on institutions,

organizational behavior, and decision-making. Social science research can

complement forest resource managers’ efforts to innovate and adapt according to changing social contexts.

Three Problem Dimensions Worth Exploring

1. Who is the public?

A primary challenge facing forest resource managers is to identify the relevant public stakeholders who may be affected by or interested in the effects

1Department of Forest Sciences,

Colorado State University, Fort Collins,

CO.
210 USDA Forest Service Proceedings RMRS-P-29. 2003.

of fuel treatments. A rich array of social assessment tools and data sources are readily available to address this challenge. Obviously, the general public needs

to be understood, especially concerning fuel treatments on public lands. However, resource management decisions are not necessarily popularity contests.

Additionally, random sample general public surveys tend to aggregate responses

across many important variables, such as knowledge of forest conditions and

forest fire risk, risk perception, and geographic context, to name a few.

Public stakeholders that may have a more direct bearing on the appropriate

place and timing of fuel treatments are communities, residents, and landowners directly affected by forest fires and fuel treatments. Such stakeholders tend

to be more vocal and mobilize in response to activities that run contrary to

their values. The challenge faced by forest resource managers vis-à-vis these

stakeholders is to listen, understand, and work with the stakeholders towards

productive courses of action. Of course, every community is different and the responses of community stakeholders to fuel treatment efforts can differ significantly depending on the context – geographic, social, economic, and

political.

The importance of context cannot be overstated. Public understanding and

acceptability of fire and fuel treatments tend to be “flashy” or episodic based

on recent events. For example, people with immediate experience with the

Cerro Grande fire of 2000 will have very different views on fuel treatments

than individuals for whom the fire was a distant occurrence. Across communities, there are wide differences in expertise, capacity, and leadership to address

unhealthy forest conditions and mitigate fire risk. One can imagine communities lying along a gradient of knowledge, capacity, and leadership. Two

communities just 10 miles apart can have very different recognition of problems and understanding of options available to them.

2. Fuel treatments: the solution to what problem?

Despite the recent attention to fuel treatments among forestry professionals in the Intermountain West, questions remain about the true goals of such

treatments, especially on public lands. The different perceptions of goals mir- ror the deep-seated conflicts over the purpose of public lands in general: Are

they to be managed for multiple human uses or to be protected as the nation’s

remaining biological heritage in the midst of landscapes long dominated by

humans?

Again, context matters: specific treatments and prescriptions may be acceptable at a general level, but there are likely significant differences across

particular contexts. For example, one may support thinning out dense stands

of ponderosa pine across the Rocky Mountain West, but would resist logging

“in my backyard.” Why the change of heart?

A medical analogy may be useful. Prescribing morphine to dull intense pain

and minimize suffering is generally accepted as sound practice. However, should

a doctor prescribe morphine to cure a headache? A morphine prescription is a

treatment, but the treatment depends on the nature of the problem. Therein

lies a significant difference between forest resource management and medicine: reaching consensus on the nature of “the problem” is often elusive and

the source of intractable conflict. Even in the wake of catastrophic wildfires,

there remains public debate over the true nature of “the problem.” Is it to

protect private property? To protect public safety and welfare? To restore a

small area of forest like a domestic watershed or across a large landscape like

the entire Interior West? Forest resource and fire managers should not take

for granted the existence of a consensus on the definition of the problem.

Indeed, a conference entitled, “Reclaiming the concept of forest restoration

Cheng Fire Social Science Research: Opening Remarks
USDA Forest Service Proceedings RMRS-P-29. 2003. 211

on public lands” is being held right after this conference in Boulder. It is

being organized by a coalition of conservation groups and will highlight key

differences in how the “forest health” problem is being defined in the public

policy process.

3. Integration of science and public values

Forest management has always been a blend of science and values, yet the

process of blending science and values has often been arbitrary and unsystematic. One result has been the increased intervention of the legislative and judicial

systems in removing discretion and judgment from resource professionals in

favor of highly regimented statutes, regulations, and procedures. A second

result is that resource professionals are placed in the role of arbiter among

competing stakeholder claims – the classic “loggers versus environmentalist”

split being one such competition. Integrating science and public values can

become obscured, leading to decisions that are neither technically sound nor

socially acceptable.

Efforts are being undertaken across the West to move beyond “analysis

paralysis” towards more collaborative approaches to defining and addressing

problems related to unhealthy forests and fire risk. However, collaboration is

easier said than done. Collaborative efforts are time-consuming and often do

not produce expected results – perhaps because expectations are unrealistically high for collaborative processes. Much work remains to be done in

designing and evaluating collaborative planning processes and adaptive management strategies. One thing is clear: there is no one universal model.

However, collaborative processes hold the promise that determining the appropriate places and times for fuel treatments can be widely supported and

readily implemented. The sustainability of forest ecosystems may indeed depend on collaborative processes making honest and earnest efforts.

Summary

Determining the appropriate places and times for fuel treatments to address

forest fire risk occurs in a complex social context. Understanding and effectively engaging within this context is imperative for forest resource managers.

Social science perspectives and research methods should not be considered

addenda or afterthoughts in developing fuel treatment plans, as they can offer

insights on public perceptions and strategies for effectively engaging public

stakeholders.

Fire Social Science Research: Opening Remarks Cheng
212 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 213

People and Fire in Western Colorado:

Methods of Engaging Stakeholders

Sam Burns1, Chuck Sperry2, and Ron Hodgson3

Abstract—In the context of the National Fire Plan, greater attention should be given

to the engagement of communities in mitigating catastrophic wildfires. An overview

is presented of a study in Western Colorado based on over 25 focus groups. This

study seeks to discover improved ways to foster participation and ownership among

local citizens and stakeholders in fire prevention and education efforts. The focus

group process addressed local defintions of the “wildfire problem,” community values placed at risk by wildfire, conditions and resources that would facilitate greater

community participation in dialogue and action, and recommended fire prevention

messages and methods of communication and education.

Introduction

The conduct of social science research about fire behavior and management should be placed in the context of the growing involvement of

communities in stewardship improvements on public lands, or what in many

circles is being called community-based forestry. In so doing, the focus of

community-oriented research shifts from viewing people as mere respondents

to a set of study questions toward being participants in a potential or anticipated community engagement process. This reorientation seems especially

relevant in the additional context of the National Fire Plan, because of its

mandates for greater involvement by citizens in addressing common resource

management concerns in the community-public land interface.

Let us consider community-stewardship, civic engagement, and the National Fire Plan as an integrating context for the research project known as

“People and Fire in Western Colorado,” an inquiry that addresses how more

meaningful community conversations might be pursued about catastrophic

wildfire prevention and mitigation.

Community Stewardship and Civic Engagement

Attention to community stewardship is a growing phenomenon in natural

resource planning and management. At the heart of this process is the basic

principle that people, communities, and the surrounding landscapes need to

be connected if they are to be mutually sustainable. (See Gray et al. 2001.)

Whether the specific form of stewardship relies upon public participation, civic

engagement, collaborative learning, community development, alternative conflict resolution, community action, or action research, the fundamental intent

is to build new forms of problem-solving relationships whereby community

1Office of Community Services, Fort

Lewis College, Durango, CO.

2The Rocky Mountain Center for Economic Democracy, Florence, MT.

3Fire & Aviation Management, Bureau of

Land Management, Lakewood, CO.
214 USDA Forest Service Proceedings RMRS-P-29. 2003.

members engage authentically with natural resources issues and goals. In other

accounts, the theme of linking communities and public lands is paramount.

(See the Four Corners Public Land Partnership program descriptions available at www.fourcornersforests.org, or Office of Community

Services-Evaluation Reports at Fort Lewis College http://ocs.fortlewis.edu.)

Gary McVicker, a person well known in Colorado for his leadership in promoting community-based stewardship efforts, notes that traditional processes

of land use planning place the lead planning organization in “the center of

competing interests, ... but they have largely failed to win the support and,

more importantly, ownership from these competing interests.” McVicker

believes that much of public land use planning can be characterized as “more

an investment in formal decision-making than in public consensus; demanding more and more information to satisfy public interests; hampered by

administrative appeals; costly, and not tied to agency budget processes for

implementation; and losing public interest and support” (McVicker, Unpublished paper.)

Margaret Shannon, a leading spokesperson for collaborative stewardship of

public lands, in expressing her concerns about how public land interests

become framed as private interests, says that “current political institutions

which reward interest driven behavior...must be redesigned so as to require

civic conversation when the public good is at stake” (Shannon 1992.)

As we empirically consider the range of public perspectives about fire

behavior and management in local communities, should we not be thinking of

developing a conversation among stakeholders, leading to community stewardship? Should our fire prevention education process not attune itself more

clearly to community conversations, rather than stop at the content of the

message? (See “A Civic Conversation about Public Lands: Developing

Community Governance” by Sam Burns in Gray et al. 2001.)

The Community as the Context of the National Fire Plan

The central focus of the National Fire Plan, the “wildland-urban interface,”

is by definition a community issue or concern. Whatever goals, plans, and

actions are developed to reduce the risk of destructive fires at the border

between public and private lands will require the support of many community

groups, interests, or stakeholders. The hope that the National Fire Plan will

change management emphasis from fire suppression to fire risk reduction

underscores the need for greater participation and ownership on the part of

communities.

Not only does this process need to involve communities in a collaborative

planning process, it also needs to address long-term stewardship of larger scale

ecosystems, and building economic capacity to reduce and utilize fuels

removed from landscapes and watersheds adjacent to communities. (See case

study on the Ponderosa Pine Forest Partnership, Richard and Burns 1999.)

Greg Aplet, a forest ecologist with the Wilderness Society, emphasizes the

critical role of communities in the National Fire Plan when he notes that of

the four primary actions needed, three of them are the responsibility of local

communities. He notes:

“First, we must protect our communities...

Second, we need to determine where the places are where we can still allow

fire to play its natural role...

Third, we must restore fire through prescribed burning in those forests

whose structures will allow the safe reintroduction of fire...

Burns, Sperry, and Hodgson People and Fire in Western Colorado: Methods of Engaging Stakeholders
USDA Forest Service Proceedings RMRS-P-29. 2003. 215

Finally, on those parts of the landscape that will not burn safely, we must

begin the process of mechanically treating fuels to create a structure that eventually will accept characteristic fire.”

Aplet concludes, “As I review these steps, it occurs to me that only one of

them, the management of those places where we will allow fire to burn, is

primarily a federal responsibility. The other three will require unprecedented

cooperation of multiple stakeholders and levels of government to achieve”

(Aplet 2001, p.5).

Since the introduction of the principles of “community based ecosystem

management,” (see Gray et al. 2001), the stewardship capacity of local communities has been increasingly recognized. Furthermore, when placed in the

context of the wildland-urban interface goals of the National Fire Plan, such

principles become even more paramount, due to the heightened need for

public-private cooperation and investment.

The National Fire Plan calls for increased action by communities in both

planning and stewardship to reduce the risks of catastrophic wild fires.

Citizens and leaders are being given increased opportunities to prioritize where

risks exist, where fuel reduction efforts should occur, and the degree and scope

of the fuel treatments. In Southwest Colorado, the five counties of Archuleta,

Dolores, Montezuma, La Plata, and San Juan have developed, with support

from the San Juan National Forest, county fire plans that identify high-risk or

fire prone areas on private and public lands and propose a range of collaborative mitigation and prevention actions (available through the Office of

Community Services, Fort Lewis College or see on line at

Southwestcoloradofires.org). As the National Fire Plan is implemented, and

as community interaction and partnerships evolve, there is increasing awareness that the values, attitudes, and knowledge held by community members

about natural and prescribed fire are key components to successful mitigation

of catastrophic wildfire. Why is this true?

The ways in which citizens and policy makers understand the role and significance of fire, and the condition of surrounding ecosystems, continuously

affect the goals, strategies, and actions that they deem appropriate to reduce

wildfire risk and to restore forested and rangelands to sustainable levels of

health. If no collaboration is created in the wildland-urban boundary area,

effective, public-private stewardship will be diluted, if not totally derailed.

Orientation of the Research Project

Previous community-oriented, social science research has tended to focus

on public perceptions or acceptability of various fire management strategies.

(See Cortner et al. 1981 and Machlis et al. 2002, which addresses previous

research public perceptions and acceptability of fire.) Many studies have addressed perceptions of wildfire risks to communities, attitudes toward the role

of fire in ecosystems, the degree to which managed fire will be deemed appropriate, and other topics. Since the National Fire Plan calls for resource managers

to work cooperatively with communities and citizens to manage fire behavior

and effects, it is imperative that multi-party resource stewardship efforts be

undertaken. In cross boundary situations between private, local, and federal

government entities, a lack of participation by one party will maintain existing

hazardous fuels, which will negatively impact all adjacent properties

However, involving a variety of interests in collaboratively planning and

implementing prescribed fire on public and private lands is an ambitious goal.

To begin with, many of the parties do not share a meaningful common view of

fire’s role in the natural environment, its effects, or whether public investment in

People and Fire in Western Colorado: Methods of Engaging Stakeholders Burns, Sperry, and Hodgson
216 USDA Forest Service Proceedings RMRS-P-29. 2003.

fire mitigation should become a high priority. (The debate over the proper

role of thinning and prescribed fire has continued well into 2002, after

another major wildfire season; see Kenworthy 2002 and Robbins 2002 as

recent examples of the level of disagreement about defining the “problem” or

“issue,” or potential “solutions.”)

To reach the goal of communities and fire managers (professional and volunteer) working alongside each other, improving public safety, and making

forest lands more healthy, much more needs to be understood about the values and understandings of fire held by the various interest groups. In February

2002, social scientists (see Appendix 1 at the end of this paper) met to discuss

the methodology of the People and Fire in Western Colorado Research Project.

It was proposed that a process of collaborative action and convergent understanding needs to be constructed from an array of beliefs and understandings

held by diverse interests and groups. With a deeper knowledge of such attitudes, values, and perceptions, there might be a greater likelihood of

multiple-party cooperation in addressing wildland-urban fire mitigation.

(figure 1).

As indicated by this model, the orientation of the proposed research is to

discover from communities how best to establish a relevant dialogue about

fire mitigation and prevention; that is, how to better create the civic conversations needed to produce a multi-stakeholder community fire plan. Achieving

this civic dialogue has obvious implications for the methods and content of

fire prevention education.

Convergence on what

the landscape should

look like and how fire

should behave there.

Collaboration

Common Understanding

and opinions about fire

and fire effects.

Diverse Range of Stakeholder Perspectives

▼

▼

▼

Stage 4:

Stage 3:

Stage 2:

Stage 1:

Figure 1—Process for collaborative

action and convergent understanding.

Objectives

The West Slope Community Fire Research Project has two specific steps or

phases:

• To identify the relevant individuals and organizations that have interests in

fire, natural and prescribed; have a stake in how fire occurs and is managed;

Burns, Sperry, and Hodgson People and Fire in Western Colorado: Methods of Engaging Stakeholders
USDA Forest Service Proceedings RMRS-P-29. 2003. 217

and could play an active role in community efforts to reduce risks of catastrophic wildfire.

• To gather and document the values, attitudes, and knowledge held by these

interests and stakeholders through facilitated group discussions in a manner

which takes into account the social, economic, and cultural diversity of

Western Colorado.

These objectives will constitute the two phases of the overall research project.

The stakeholders will first be identified, and then a series of discussion or

focus groups will be held within several natural, social areas of each subregion or study area (figure 2).

Figure 2—Study areas in Western

Colorado.

People and Fire in Western Colorado: Methods of Engaging Stakeholders Burns, Sperry, and Hodgson
218 USDA Forest Service Proceedings RMRS-P-29. 2003.

Strategy

The study region for research on community and social understandings of

fire is the Western Slope of Colorado, in essence that portion of the state lying

west of the Continental Divide. This area consists of 21 counties, which will

be divided into five sub-regions based on watersheds, economic patterns, and

demographic and lifestyle characteristics. Throughout Western Colorado, the

federal government, under the auspices of the U.S. Forest Service, the Bureau

of Land Management, and the National Park Service, manages most of the

land base. The tribal lands of the Southern and Ute Mountain Ute Tribes also

contribute to a low percentage of privately owned lands, which in some counties can be under 10 percent.

The Western Slope in general can be described as a region in transition

from an economy based in agriculture and mining to one linked to tourism,

retirement communities, and recreation. However, the pace of this transition

is markedly different throughout the Western Slope, which further underscores the need to assess the values and understandings of community members

about fire, fire risk, and management within distinct sub-regions.

In order to begin the process of gathering the knowledge and understandings that could become the basis of a convergence-collaborative process,

approximately 25 focus groups will be conducted in the 21 counties in

Western Colorado. The counties will be divided into five study areas as per the

attached map. A single two-hour meeting will be held in selected communities within each natural topographic-social region.

Diverse stakeholders will be chosen from a variety of interest areas such as

recreation, wildlife, real estate, and local government. However, and perhaps

more importantly, stakeholders will be selected for a balanced knowledge of

both community and fire issues. Stakeholders need not be formal community

leaders or professional experts about fire, although there could be some of

these persons represented. It is preferable that the various interest-oriented

stakeholders include persons who know something about citizen concerns

about surrounding forest lands, about beliefs regarding natural and prescribed

fire management, and about what it would take to reach common understandings about wildfire mitigation planning and decision making.

While focus group members may have strong views of their own about

reintroducing fire into surrounding ecosystems, or thinning the lands adjacent to a given community, they should also be open to listening to other

viewpoints in a dynamic group discussion. Most strategically, they should be

willing to assist in describing what others believe or think about fire and

appropriate management solutions, in a manner that could be utilized to build

convergence and collaboration around a community based fire management

plan.

The sample for this research will not be chosen randomly and evenly throughout Western Colorado. Rather, the participants will represent the attitudes

and values of the social and cultural places where they live and work, or what

many analysts refer to as a “sense of place.” Places in Western Colorado vary

dramatically as a result of recent economic and demographic changes. There

are traditional ranching communities like the west end of San Miguel County,

and second home enclaves like Aspen. Among these and many other communities, there are quite different relationships with the surrounding forestlands.

(See Swanson 2001 and the socio-economic data profiles on communities in

Southwest Colorado collected as a part of the San Juan National Forest Plan

Revision, 1996-98.)

Burns, Sperry, and Hodgson People and Fire in Western Colorado: Methods of Engaging Stakeholders
USDA Forest Service Proceedings RMRS-P-29. 2003. 219

Focus Group Inquiry

Four areas of inquiry will be pursued with each focus group:

 Framing the Issues

• From your perspective, what is the wildfire problem or issue?

• Do you see wildfire as a problem, or merely an issue?

• Do some people in the community not see wildfire as a problem?

• How do various groups in your community view the wildfire problem-issue?

• What terms do people use to frame or describe the wildfire problem-issue?

• To whom do people attribute responsibility for the wildfire problem-issue

and/or possible measures to reduce risk or threats, as they see them?

Community Values

• What locally held values cause people to think that wildfire is a concern, in the

sense that those values could be threatened or might be compromised by wildfire?

• Do certain groups hold these values in particular? For example from a governmental perspective, or any specific interest group positions?

• Do you have a sense of what the most important community values are

related to wildfire and improving community safety?

Capacity for Community Dialogue

• What conditions would need to exist in your community, in order for you

and others to develop a productive dialogue on fire issues and/or any actions

to reduce community risks? Examples of “conditions” could be a level of trust

among key parties, a sense that participation in the dialogue would result in

productive outcomes, or having reasonable access to information and knowledge about fire risk and environmental conditions. (There could be many

other types of conditions.)

Education

• What do members of your community need to know to begin to talk productively about the wildfire issues and potential measures to improve

community safety?

• Where do people prefer to obtain information about community issues of

this nature? (Radio, TV, newspaper, workshops, etc.)

• Are any particular means or methods of receiving information more acceptable to community members than others? (Brochures, videotapes, group

presentations, field trips, etc.)

• Are you, or others you know, willing to be a part of a monitoring group that

would visit sites where efforts are being made to reduce wildfire risks in your

community as a part of a learning dialogue?

Engaging Stakeholders

In the conduct of community planning and decision-making processes, it is

rather routine to ensure that stakeholder identification is representative with

regard to a broad range of socio-economic and demographic characteristics.

Typically, stakeholder selection would take into account employment, length

of residence, political power, and age, among many other societal dimensions.

While the importance of these factors is unquestioned in considering the

democratization of resource stewardship, in the context of developing community based fire mitigation efforts, this research project will emphasize a

People and Fire in Western Colorado: Methods of Engaging Stakeholders Burns, Sperry, and Hodgson
220 USDA Forest Service Proceedings RMRS-P-29. 2003.

stronger sense of community capacity building, establishing civic dialogue,

networking, relationship formation, and public conversation, and will therefore strongly influence the stakeholder engagement process.

In this light, the West Slope Fire Research Project has established a stakeholder identification and selection process based on the following conditions,

assumptions, and attributes:

• A priority will be placed on stakeholder knowledge of local communities

and their values.

• Similarly, stakeholders will be selected because they have a substantial

degree of knowledge about fire and fire management, although they will not

necessarily be “experts” in a professional or scientific sense.

• Engagement of stakeholders will occur on the basis of their active participation in envisioning and creating a civic conversation about fire impacts and

mitigation measures.

• Stakeholders will be looked upon as representing communities of place,

having social, experiential, and historical knowledge of a particular place, rather

than as isolated, individual respondents who are merely sources of data or

factual information.

• Utilizing local organizations, which facilitate the stakeholder nomination

and selection process and serve as conveners of the focus groups, will increase

the capacity of individual communities and regions to collaborate with fire

management and education staff in the ongoing implementation of the

National Fire Plan.

• Stakeholders will be given the option of serving as monitors in subsequent

fire-risk reduction and education efforts, if such opportunities become available in their community study area.

Limitations and Challenges

Approaching the stakeholder identification and selection from a community action and development perspective, and seeking knowledge in an evolving

local context, also present numerous challenges:

• Consideration has had to be given to coordinating with community fire

planning that is in progress in many of the study areas. Some communities are

just getting underway and others are nearly completed.

• In some cases, local fire management staff are reluctant to support a project,

which they perceive as merely “research,” rather than one that actually gets

work completed on the ground.

• Simply creating a network of stakeholders on a one-time discussion basis

may create longer term public expectations about civic engagement that need

to be recognized and appropriately addressed through opportunities to participate in fire management planning.

• Creating a stakeholder group of diverse interests in a region also raises other

issues, such as identifying communication barriers among various private, state,

and federal jurisdictions, which affect the research outcomes in both short

and long term ways.

• Communities may experience large catastrophic wildfires, accentuating in

the minds of many the urgency for community action and work with fire managers.

• In essence, when working within an action-oriented stakeholder-based research process, the research step often blurs into “action thinking,” to the

Burns, Sperry, and Hodgson People and Fire in Western Colorado: Methods of Engaging Stakeholders
USDA Forest Service Proceedings RMRS-P-29. 2003. 221

extent that in many communities there could be heightened interest in immediate or timely feedback of the project findings.

Summary Perspectives

Over the past 20 or so years, numerous studies have been undertaken with

regard to social and communal values concerning fire risk, fire behavior and

consequences, and fire management activities. Beginning in December of 2001,

these studies were reviewed in order to discover previously asked questions

and research findings. This review of previous social science research was

compared and contrasted with several contemporary community fire planning efforts to prepare research agendas on the social aspects of fire, as

well as recent conference proceedings. These findings were utilized as the

basis for determining the focus of this research by the social science advisory team.

Within Western Colorado, five sub-regional study areas were chosen based

on an analysis of river basins, social and economic characteristics, and other

aspects of social and cultural geography and senses of place. These areas include Southwest Colorado south of the San Juan Mountains; the Uncompahgre

and Gunnison Valleys from Ouray north to Grand Junction and running west

to the Utah border; the high mountain areas between Glenwood Springs,

Aspen, and Eagle along the I-70 corridor; and the northwest quadrant from

Rio Blanco County east to the Routt County (figure 2). Within each of these

five sub-regions, from three to five communities were chosen to conduct the

facilitated group discussions.

Stakeholders were identified utilizing a wide range of networks within each

sub-region. These include specific land and resource user groups, local government officials and staff, emergency management personnel, healthy

community organizations, civic and non-profit groups, and wildlife and other

conservation associations, among others. Stakeholder identification is being

completed with an eye towards grounding the research process within local

groups and networks, in anticipation that they can continue to participate in

follow-up education, fire demonstration, and monitoring activities.

In February 2002, a meeting of social scientists was held in Fort Collins,

Colorado, for the purpose of designing a protocol for the focus groups. This

advisory team assisted in identifying research topics, key questions, and a scope

of inquiry, which will be practical and advantageous to pursue in the group

discussions. The discussion protocol served as a guide for the facilitators who

conducted the focus groups, while allowing for local adaptability to fit special

social and historical conditions.

Summaries of each group discussion were prepared. These were then collated into five study area reports, and finally into a Western Slope (the

geographic area of Colorado west of the Continental Divide) set of findings

and outcomes. The summaries will be made available to the local and constituent organizations, state and federal natural resource management staff,

and fire education specialists for use in ongoing efforts to reduce catastrophic

fire risk and implement local mitigation and stewardship practices.

The obvious question we have is whether engaging stakeholders for the

purpose of developing community conversations and action about fire management will produce a different type and quality of local knowledge from

standard survey research. Will this community-oriented stakeholder identification approach contribute to increased civic engagement and stewardship in

the context of the National Fire Plan? Will this process of selecting persons

People and Fire in Western Colorado: Methods of Engaging Stakeholders Burns, Sperry, and Hodgson
222 USDA Forest Service Proceedings RMRS-P-29. 2003.

with community knowledge and fire awareness have any implications for how

to reach the less attentive and informed public?

We believe that the relevance of this research approach is the potential of

creating a model for enhancing community capacity to engage in collaborative, fire mitigation planning in the wildland-urban interface.

References

Aplet, G. 2001. Fire planning and funding needs are local, not Federal. Communities

and Forests, newsletter of the Communities Committee of the Seventh American

Forest Congress. Fall issue.

Birchfield, Jim, ed. 2000. National Conference on the Social Acceptability of Fuel

Treatments on Western Public Lands. October 22-24, 2000. Sponsored by the

Bolle Forestry Center, University of Montana.

Cortner, H.; Zwolinski, M.; Carpenter, E.; and Taylor, J. 1981. Public support for

fire management policies. Journal of Forestry. 82(6): 359-361.

Gardner, P., Cortner, H., Widaman, K., and Stenberg, K. 1985. Forest-user attitudes

towards alternative fire management policies. Environmental Management. 9(4):

303-312.

Gray, Gerald J.; Enzer, Maia J.; and Kusel, Jonathan, editors. 2001. Understanding

community-based forest ecosystem management. Binghamton, New York: Haworth

Press Inc. (Food Products Press).

Kennworthy, Tom. 2002. US fire policy isn’t cutting it. USA Today. August 22.

Machlis, Gary, et al. 2002. Burning questions: A social science research plan for Federal wildland fire management. National Wildfire Coordinating Group.

McVickers, G. No date. Community based partnerships and environmental health: A

vision for change. Unpublished manuscript.

Moote, A. 2002. Community-based forest restoration: Three case studies in the Southwest. forthcoming in a guidebook on forest restoration. The Ecological Restoration Institute: Northern Arizona University.

Richard, Tim and Burns, Sam. 1999. The ponderosa pine partnership: Forging new

relationships to restore a forest. Durango, CO: Fort Lewis College, Office of Community Services.

Robbins, Jim. 2002. Forest thinning challenged as a tactic to control fires. New York

Times. August 27.

Shannon, M. 1992. Community governance: An enduring institution of democracy.

Proceedings and Summary of the Workshop entitled Multiple Use and Sustained

Yield: Changing Philosophies for Federal Land Management? (Printed for use by

the Committee on Insular Affairs, U.S. House of Representatives.) Pp 219-250.

San Juan National Forest Plan Revision. 1996-1998. Socio-economic data/community profiles. Durango, CO: Fort Lewis College, Office of Community Services.

Swanson, Larry. 2001. The West’s forest lands: Magnets for new migrants and parttime residents. Changing Landscape. 2(1): 16-21.

Burns, Sperry, and Hodgson People and Fire in Western Colorado: Methods of Engaging Stakeholders
USDA Forest Service Proceedings RMRS-P-29. 2003. 223

Appendix 1

Timeframes

The research project was initiated through an assistance agreement between

the Office of Community Services at Fort Lewis College and the Colorado

Office of the Bureau of Land Management in September of 2001. The following time frames serve as the general implementation schedule for the project:

• October 2001-March 2002 / Research Design, Resource Identification

and Contracting: During this period, previous research was reviewed, key resource persons in Western Colorado were contacted, a science advisory

committee met and made recommendations on research questions, and research contractors were selected.

• March 2001-April 2002 / Stakeholder Documentation: The stakeholder

identification phase will be completed, utilizing five sub-regions to focus the

inquiry.

• May 2002-November 2002 / Discussion Group Analysis: Within each subregion, a series of facilitated group discussions will be held to describe and

document the values and perspectives of the various interests of individuals

and groups about fire.

• September 2002-February 2003 / Analysis and Reporting.

Social Science Advisory Team Participants

Name Affiliation E-mail Phone

Michele Burns CSU, Forest Sci. Mitch409@angelfire.com 970.491.7854

Sam Burns Fort Lewis College Burns_s@fortlewis.edu 970.247.7193

Tony Cheng CSU Forest Sci Faculty Chengt@cnr.colostate.edu 970.491.1900

Hanna Cortner NAU Ecological Hanna.cortner@nau.edu 928.523.8533

Restoration Institute

Judith Downing USFS/BLM Jldowning@fs.fed.us 303.445.4360

State-Private Forestry

Shana Gillette USGS – CSU Shana_gillette@usgs.gov 970.226.9308

Drew Hardy CSU, Forest Sci. Drew.hardy@colostate.edu 970.493.5967

Christy Higgason CSU, Forest Sci. Christy@cnr.colostate.edu 970.491.1900

Ron Hodgson BLM-Colorado Ronald_hodgson@co.blm.gov 303.239.3851

Chuck Sperry Consultant-Montana Chuck@romced.org 406.273.0988

Jonathan Taylor USGS-Fort Collins Jonathan_taylor@usgs.gov 970.226.9438

People and Fire in Western Colorado: Methods of Engaging Stakeholders Burns, Sperry, and Hodgson
224 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 225

From Analysis Paralysis to Agency-Community

Collaboration in Fuels Reduction for Fire

Restoration: A Success Story

Timothy Ingalsbee1

Abstract—In 1996, the Ashland Ranger District of the Rogue River National Forest

proposed the HazRed Project to expand a shaded fuelbreak system within the Ashland

municipal watershed. The original proposal sparked intense community opposition

and was withdrawn following administrative appeals. The Forest then proposed the

Ashland Watershed Protection Project and used collaborative methods to generate

continuous substantive public input. When a final decision was issued in 2001, the

Project had gained enthusiastic community endorsement with volunteers helping to

implement it on-the-ground. This story offers useful lessons for successfully overcoming “analysis paralysis” in fuels reduction and forest restoration projects.

Introduction

I

n the winter of 2001, former chief of the U.S. Forest Service Jack Ward

Thomas complained in testimony before Congress that the Forest Service was suffering from “analysis paralysis.” The intended message was that it

was becoming increasingly difficult for forest managers to implement management projects in a timely fashion due to a burdensome number of conflicting

environmental regulations requiring lengthy public processes. In the public

policy literature, “analysis paralysis” is a concept referring to an overload of

data that makes it difficult to analyze effectively. The problem of the Forest

Service, it is argued, is not due to an overload of data, but an overload of

public controversy. Controversies are often generated by management proposals that involve commodity timber extraction, especially when these projects

are presented as something else such as fire hazard reduction or forest restoration projects.

Individuals and organizations affiliated with the conservation community

have been particularly adept at asserting their rights under agency regulations

and the nation’s environmental laws to prolong environmental analyses and

decision-making. Forest Service decisionmakers sometimes misinterpret the

public opposition to commercial logging as opposition to all forest management in general. The need for hazardous fuels reduction and forest ecosystem

restoration, however, has created new management opportunities for both

conflict and cooperation between federal agencies and local communities. The

following paper will present the story of how a progressive Forest Service

Ranger and a conservation-minded local community were able to teach and

learn from each other, and transcend “analysis paralysis” over a contentious

timber sale proposal, to eventually reach consensus on a restoration-oriented

fire hazard reduction project within a municipal watershed. It promises to

become a model of agency-community collaboration in fire restoration work,

1Western Fire Ecology Center, American

Lands Alliance, Eugene, OR.
226 USDA Forest Service Proceedings RMRS-P-29. 2003.

with long-term ecological and social benefits to the local landscape and wider

society.

The HazRed Project

Draft Environmental Assessment

The Ashland Ranger District issued a scoping notice on July 5, 1996, for

the “Ashland Interface Fire Hazard Reduction (HazRed) Project.” The purpose and need for the HazRed Project was to “reduce fire hazard levels in

strategic areas to protect values at risk of being lost to large-scale stand-replacing

fire.” The Draft Environmental Assessment (EA) was issued in February 1997

and proposed to treat 1,631 acres with a mix of commercial logging, manual

cutting and piling, and prescribed underburning in order to construct and

expand a ridgeline shaded fuelbreak system in the interior of the watershed.

The fuelbreak would have reduced canopy closure to 30-40 percent, leaving

an average 20-30 foot horizontal spacing between the crowns of dominant

and codominant trees. All snags and large downed logs would be removed,

and woody material on the forest floor would be reduced to an average of

1.5 tons per acre. The stated purposes of the fuelbreaks were to allow the safe

deployment and evacuation of firefighters, increase the penetration of fire

retardant through the forest canopy, and reduce the spread of running

crownfires [Draft EA; p.4]. The Project would also have involved road and

helispot construction in order to facilitate skyline and tractor yarding systems,

and to shorten helicopter yarding distances.

The Ashland Municipal Watershed

Commercial logging is highly restricted in the Ashland Watershed and there

were several indications that the proposed HazRed Project would spark significant controversy. First, the Project area is managed as a restricted watershed

since it is the primary domestic water source for the City of Ashland. The City

and the Forest Service have a cooperative agreement dating back to 1929 that

requires the agency to consult with City officials prior to any plans to remove

timber or other forest products from the Ashland Watershed. The watershed

is characterized by steep, unstable slopes of decomposed granitic soils with

naturally high rates of erosion, often in mass debris flows that dump sediment

directly into streams. Just before the Draft EA was scheduled to be released,

the watershed experienced a major rain-on-snow event that resulted in a

30 year flood event on New Year’s Day in 1997. Many landslides were triggered alongside logging roads, the downtown commercial district was flooded

by several feet of water, and the City was forced to import potable water for

several weeks. The Draft EA alarmed the community since many of the proposed fuelbreak units were rated as having extreme landslide hazards and in

some cases were located directly above active landslides.

Second, the project area was located inside a critical habitat unit and latesuccessional reserve (LSR) established by the Northwest Forest Plan to conserve

habitat for the northern spotted owl and other old-growth associated species.

The Mt. Ashland LSR, coincidentally, is a critical node or “crossroads” in the

LSR system, linking the high elevation Siskiyou range of the Klamath mountains with the southern portion of the Oregon Cascades and the Oregon coast

range. Commercial logging is highly restricted and intensely controversial in

LSRs. Forest Service staff predicted that timber extraction for the fuelbreaks would

cause long-term habitat degradation for eight pairs of northern spotted owls.

Ingalsbee From Analysis Paralysis to Agency-Community Collaboration in Fuels Reduction for Fire Restoration: A Success Story
USDA Forest Service Proceedings RMRS-P-29. 2003. 227

Third, the Ashland Watershed is located in the Klamath-Siskiyou bioregion,

a proposed world heritage site renowned as one of the richest areas of

biodiversity in the North American continent. Fire has played a major evolutionary role in shaping species composition, stand structure, and the amount

and distribution of live vegetation and dead fuel in the region. The watershed

is comprised of a fire-dependent mixed-conifer ecosystem with a natural fire

return interval of 8-15 years; however, the Forest Service has managed the

watershed for fire exclusion since the early 1900s, resulting in 4-9 missed fire

cycles [Draft EIS; pg. I-6] (USDA FS 1999). Part of the expressed need for

the HazRed Project was to compensate for the effects from past fire suppression that resulted in excessive hazardous fuel loads. Ironically, the purpose of

the fuelbreaks was to increase suppression effectiveness and continue fire exclusion.

Finally, the Ashland Watershed has prime recreational, scenic, and spiritual

values for the local community. Known affectionately as “the forest at Ashland’s

doorstep,” local residents have a strong sense of place and personal connection with the watershed. Unlike most other rural communities in southern

Oregon, Ashland’s economy is not dependent on the timber industry; on the

contrary, it is the home of Southern Oregon University and the renowned

Oregon Shakespearean Festival which attracts thousands of students and tourists, generating $45 million in revenue each year. Ashland has a reputation for

being a wealthy, liberal community supportive of environmental and social

justice causes. Any timber sale would have been controversial and engendered

the opposition of local residents philosophically opposed to any commodity

resource extraction in the watershed for any reason.

For all the above reasons, conservationists recognized the HazRed Project

as both controversial and precedent-setting and thus questioned the legality

of the Forest Service issuing an EA instead of an Environmental Impact

Statement (EIS) for the Project. In each case, though, the Forest argued that

the impacts on water quality, soils and slopes, spotted owl habitat, and scenic/

recreational values from a potential “catastrophic wildfire” far outweighed the

minimal impacts to be caused by commercial logging for fuelbreak construction.

Public Involvement in the HazRed Project

The local nonprofit conservation organizations, Headwaters and the

Klamath-Siskiyou Wildlands Center (KSWC), were instrumental in generating community involvement in the HazRed Project. Headwaters organized

some early protest demonstrations and public rallies in 1997 and wrote several

articles critical of the Project’s potential environmental impacts in the

organization’s quarterly newsletter. The KSWC sponsored public hikes through

proposed fuelbreak units where people observed old-growth sugar pines up

to 6 feet in diameter at breast height (DBH) had been marked for cutting,

along with over 4,400 trees greater than 20 inches DBH, and nearly all of the

understory white fir trees. The sentiment of the environmental community

was that the marking was excessive and that the Project was essentially a timber sale, not a fire hazard reduction or forest restoration project. A coalition

of environmentalists submitted their own alternative during the comment

period that would have put a diameter cap of 17 inches DBH for trees able to

be extracted. Finally, the Ashland Mayor and City Council submitted a comment letter with a number of suggestions that echoed many of the conservation

community’s concerns, including the desire to construct no new shaded

fuelbreaks and to focus on reducing surface fine fuels and brush rather than

extracting large trees.

From Analysis Paralysis to Agency-Community Collaboration in Fuels Reduction for Fire Restoration: A Success Story Ingalsbee
228 USDA Forest Service Proceedings RMRS-P-29. 2003.

The community’s fear that the HazRed Project would be followed by additional timber sales in the watershed in the future was prompted by the fact

that one of the uses of the Project’s timber sale revenues was intended to pay

for completing an earlier so-called hazard reduction project. The Helikopter

Salvage Sale had been logged in 1990/91 but had left behind thousands of

submerchantible trees and had left untreated over 200 tons per acre of logging slash, which contrasted with the natural fuels accumulations of 10-35

tons per acre in unlogged sites [Draft EIS; pg. III-1] (USDA FS 1999). As is

the case with nearly every proposed hazard reduction project involving a timber sale, the Forest Service intended to do the commercial logging first, then

perform the restoration activities later using the timber sale receipts. This only

fed into the public’s perception that commercial timber extraction was the

primary objective of the Project.

The District Ranger responded to the community’s concern about excessive timber marking by literally walking the units with her staff and deciding

on a tree-by-tree basis which ones specifically contributed to fire hazard, and

which ones did not. On her own initiative, the Ranger ordered thousands of

trees to be demarked, decreasing in half the amount of 20 inch DBH trees

that were going to be logged, retaining all sugar pine and cedar trees, and

allowing isolated clumps of large healthy trees to remain uncut [Final EA;

APP. B, pg.29] (USDA FS 1998). This good faith gesture struck a responsive

chord and personally endeared the Ranger among the Headwaters organization and other members of the local community. But it still did not sway the

conservation community’s belief that the HazRed Project was simply using

the rationale of fire hazard reduction as an excuse to “get the cut out.”

Final Environmental Assessment and Decision Notice

The Final EA, Decision Notice (DN), and Finding of No Significant

Impact (FONSI) for the HazRed timber sale were issued simultaneously in

March 1998. The decisionmaker announced that the Final EA was a complete

revision and cautioned readers to avoid comparing it with the Draft. A

notable and positive change was the cover of the Final EA. The cover of the

Draft EA had displayed a photograph of a huge black, billowing smoke cloud

rising above the city of Ashland during the 1959 Ashland Fire. Local environmental advocates were critical of the use of that old photo, perceiving it as an

attempt to use scare tactics to win short-term public support for the timber

sale at the possible long-term expense of community support for prescribed

burning in the watershed. In response to this criticism, the Final EA did not

use the photograph again; instead, the cover contained a candid statement

from the Ranger declaring that she had not intended the photo to be used as

“an alarmist approach to frighten citizens unduly.” Inside the document, much

of the information was reorganized and included several additional scientific

references (including papers by Agee et.al. 1996 on crownfires, and Omi 1997

on fuelbreaks). Another change was that the Ranger selected a new preferred

alternative, which had slightly less acres of logging compared to the original

proposed alternative. The DN dropped new road construction and reduced

the total acreage to be treated down to 1,472 acres, of which 457 acres were

to be commercially thinned and 1,015 were to have noncommercial treatments.

While the selected action was slightly modified and the environmental impacts were reduced, the objective of the Project remained the same: to maintain

and increase the effectiveness of the existing shaded fuelbreak system

using commercial thinning for overstory removal. The Forest Service emphasized

Ingalsbee From Analysis Paralysis to Agency-Community Collaboration in Fuels Reduction for Fire Restoration: A Success Story
USDA Forest Service Proceedings RMRS-P-29. 2003. 229

that portions of the 1959 Ashland Fire had been successfully contained along

segments of a fuelbreak cut by the Civilian Conservation Corps (CCC) in the

1930s; however, the agency downplayed the fact that only understory brush

was manually removed during the construction of that CCC fuelbreak, and

that the 1959 wildfire had also breached other portions of the fuelbreak. Regardless, the Forest held fast to its argument that a ridgeline fuelbreak system

would strategically “compartmentalize” the watershed and accomplish the

following fire hazard reduction goals: 1) reduce surface fuel conditions and

surface fireline intensity; 2) reduce fuel ladders and crown fuels that contribute to the start and spread of crownfires; 3) allow for penetration of fire

retardant; 4) provide safe areas for firefighter deployment and evacuation; and 5)

provide control points for prescribed underburning [DN; pg.3] (USDA FS 1998).

The fuelbreak strategy gained the official approval of the Regional Ecosystem

Office which provided internal oversight on projects proposed in LSRs. Indeed, the Regional Director of Fire and Aviation Management, Mike Edrington,

wrote a letter to the decisionmaker praising the HazRed Project. “We see the

HazRed project as an example of the type of treatment that must be implemented on a much larger scale in the Pacific Northwest if we are to improve

the health of fire adapted ecosystems for long term sustainability,” Edrington

wrote. Accordingly, environmentalists considered the HazRed to be a dangerous precedent, essentially functioning as a “Trojan Horse” that would justify

timber sales in all other LSRs under the guise of fire hazard reduction.

Despite internal agency approval, critics pointed to new analysis of the local

fire history that clearly showed the highest fire risk to the watershed came

from ignitions in lower elevations primarily adjacent to residential and recreational areas and roads. Furthermore, topography and prevailing up-valley

winds carried fires from the urban interface zone up into the watershed. This

was precisely the ignition and fire spread pattern of the arson-caused Ashland

Fire in 1959. Never had fires started in high elevation areas of the watershed

and then burned rapidly downslope against the wind to reach the City. Critics

challenged the logic of locating fuelbreaks in the middle of their watershed,

asking the rhetorical question, “Why would you want to build a moat in the

middle of your castle?” They preferred to locate fuelbreaks within the wildland/urban interface (WUI) zone on predominantly non-federal land where

both fuel hazards and fire risks were rated high to extreme, and where suppression actions could prevent fires from spreading into the LSR and watershed

and/or private homes.

Administrative Appeal and Withdrawn Decision

Although the Ranger had voluntarily issued an additional 30 day comment

period for the Draft EA, thereby expanding it to a full 60 days, she issued the

DN without first circulating the significantly revised Final EA for public comment. Conservationists suspected that the DN was rushed forward without a

new comment period in order to avoid an impending injunction against logging projects that was part of a successful lawsuit over the Forest Service’s

failure to comply with the Northwest Forest Plan’s Survey and Manage

requirements. The HazRed timber sale generated six appeals by environmental

organizations and a local private citizen. A number of substantive and procedural NEPA claims were raised, including the fact that there was no opportunity

to comment on the significantly revised Final EA. It was this specific issue that

in July 1998 the Regional Appeals Review Officer cited in ordering the DN to

be withdrawn.

From Analysis Paralysis to Agency-Community Collaboration in Fuels Reduction for Fire Restoration: A Success Story Ingalsbee
230 USDA Forest Service Proceedings RMRS-P-29. 2003.

Conservationists’ Critiques of the HazRed Project

It would be instructive to briefly review some of the main fire and fuelsrelated critiques of HazRed because these critiques have been raised on similar

fuelbreak construction, fuels reduction, and forest restoration projects elsewhere in the National Forest System.

Need to Develop a Wider Range of Alternatives

One of the criticisms raised against the Project was that the Draft and Final

EAs failed to contain a broad enough range of alternatives. Specifically, conservationists wanted analysis of an alternative that would have used only

non-commercial methods for hazardous fuels reduction. In fact, an alternative that would have only used prescribed underburning was originally

considered during the development of the Draft EA. The document acknowledged that underburning would have reduced surface and ladder fuels, but

this alternative was dropped from further development because it would not

have accomplished the desired canopy reduction in mid-to-large sized trees

within fuelbreaks [Draft EA; II-7] (USDA FS 1997). In order to remove

overstory trees and reduce the risk of crownfire spread, the agency insisted

that logging was simply a management “tool,” not a goal, and reminded the

public that any timber outputs generated by the Project would not contribute

to the Forest’s Probable Sale Quotient.

The claim that “logging is a tool, not a goal” is being widely repeated in

National Environmental Policy Act (NEPA) documents for Forest Service

fuels reduction/forest restoration projects. This claim is often met with considerable skepticism by environmental interest groups and members of the

public who generally mistrust government agencies. Additionally, non-logging

alternatives are rarely voluntarily developed by the Forest Service without enormous public pressure first being applied. Hence, however sincerely believed by

proponents, the assertion that “logging is only a tool” is failing to mollify critics

who still see timber extraction as the main driver of Forest Service land management decisions. Indeed, the silvicultural prescriptions for the HazRed fuelbreak

units emphasized the need for “adequate turn volume” for helicopters—

the amount of timber taken in each flight from the logging unit to the landing

zone—and specified that only trees 10 inches DBH or above would be removed. Timber markers were instructed to select a minimum of 500 board

feet contained in five or six trees within a 50 foot radius for each helicopter

load. These prescriptions clearly prioritized profitable logging operations over

effective hazard reduction, and harkened back to the Helikopter Salvage Sale

that had taken mainly commercially valuable large trees, leaving behind the

more flammable submerchantible small trees.

Need to Analyze the Environmental Effects of Fire Suppression

Since the advent of the “forest health crisis” in the early 1990s, the Forest

Service has begun acknowledging the adverse ecological effects of fire exclusion on fuel loads, stand structure, and tree stocking levels; however, the adverse

environmental effects of fire suppression have never been analyzed or disclosed in a programmatic NEPA analysis. Whenever this analysis has been

specifically requested in Project-level NEPA documents, the agency has often

claimed as it did in the HazRed Project that “fire suppression is an emergency

response activity that does not require environmental analysis to be conducted

according to NEPA regulations” [Final EA; APP. B; pg.14] (USDA FS 1998).

Conservationists urged the Forest Service, to no avail, to fully disclose the

potential indirect and cumulative effects of conducting fire suppression activities

Ingalsbee From Analysis Paralysis to Agency-Community Collaboration in Fuels Reduction for Fire Restoration: A Success Story
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within and adjacent to fuelbreaks. Doing so, they argued, would help the

agency make the case for the need for proactive fire hazard reduction projects

as a means of avoiding reactive fire suppression actions. A fundamental question had failed to be asked by the agency: are the impacts of logging and using

fuelbreaks for firefighting more or less significant than the effects of wildland

fire alone?

The Fire Management Plan for the LSR stated, “Shaded fuelbreaks do not

contain and control wildfires on their own. It takes available, trained, skilled

suppression resources to take advantage of the shaded fuelbreaks” [LSRA; D23] (USDA FS 1996). Some of the foreseeable environmental impacts that

routinely occur during suppression operations and could happen within

fuelbreaks include: habitat tree felling for cutting firelines, helispots and safety

zones; soil disturbance by heavy equipment and handcrews constructing

firelines; chemical contamination of soil and water by retardant drops and

refueling saws, pumps, and vehicles; and severe and/or homogenized fire

effects by burnout and backfire ignitions. Moreover, since “worst case” scenarios were used to analyze the effects of future wildfires, critics insisted that

“worst case” scenarios should be used to analyze the effects and effectiveness

of future fire suppression actions within fuelbreaks. This would entail total,

aggressive suppression under extreme weather conditions along the complete

length of the fuelbreaks system.

Need to Address Structural Fire Protection in the Wildland/Urban

Interface Zone

Despite the fact that HazRed was identified as the “Ashland Interface Fire

Hazard Reduction Project,” and that “Human Life and Property” was put at

the top of the list of values at risk, nowhere else in the NEPA document was

the issue of structure protection in the WUI zone addressed. The Draft EA

did not explain how human life and private property within the city of Ashland

would be protected by the proposed shaded fuelbreak located deep in the

interior of the watershed.

There have been several other proposed fuelbreak timber sales on National

Forest System lands using the rationale of “community fire protection;” yet,

general problems remain from the lack of a precise, science-based definition

of the WUI zone and lack of empirical evidence that fuel and vegetation treatments conducted several miles away from communities will in fact help protect

private structures from wildfire damage. According to research conducted by

the Forest Service’s Fire Sciences Lab in Missoula, Montana (Cohen 1999),

the prime zone for vegetation treatments to effectively and efficiently reduce

home ignitability factors is approximately 200 feet surrounding structures. In

the case of the HazRed Project, the proposed fuelbreaks would have been

constructed several miles and ridgelines away from the city, offering dubious

benefits, if any at all, to structural fire protection needs. This points to the

general need for projects to clearly demarcate fuels treatments in wildlands

conducted for ecosystem restoration purposes from fuels treatments in the

WUI zone conducted for community protection purposes.

Need to Implement the Federal Wildland Fire Policy

The 1995 Federal Wildland Fire Management Policy and Program Review

(Federal Fire Policy) and the 2001 Review and Update of the Federal Wildland Fire Policy signify a potentially profound change in federal fire management

philosophy. The letter and spirit of the Federal Fire Policy commits agencies

to genuinely move away from systematic fire exclusion toward prescribed and

wildland fire use for the restoration of fire-adapted ecosystems. The most

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urgent institutional need and highest priority action item for implementing

the Federal Fire Policy was the development of new Fire Management Plans

(FMPs). According to the Federal Fire Policy, FMPs are required for every

area on federal lands subject to wildland fire, or every acre containing burnable vegetation. These FMPs offer the strategic framework for the full range of

fire management projects and actions, from hazardous fuels reduction and forest

restoration projects to fire prevention campaigns and fire suppression incidents.

The HazRed Project failed to tier to the Federal Fire Policy or discuss how,

if at all, the Project complied with the Policy in terms of fire reintroduction

and forest restoration goals. Critics were concerned that the proposed fuelbreak

was designed solely for the purpose of containing wildfires, thus continuing

the Rogue River National Forest’s obsolete fire exclusion-based FMP in the

watershed’s fire-dependent ecosystem. Critics asserted that implementing a

fuels reduction project before the Forest first developed a new Fire Policycompliant FMP was “putting the cart before the horse.” Even worse, the two

were disconnected and heading in opposite directions: the HazRed Project

was oriented toward continued fire exclusion while a Policy-compliant FMP

should be oriented toward fire reintroduction and ecosystem restoration.

Need to Ensure Proper Fuelbreak Maintenance

An inherent challenge with extensive fuelbreak systems is the need for periodic maintenance to retard the growth of flammable native and exotic

vegetation that can thrive in exposed, logging-disturbed sites. Without maintenance, fuelbreak sites can convert from a timber fuel model to a grass or

brush fuel model and actually result in increased fireline intensity and rate of

spread, thus undermining the stated purpose for safe, efficient fire suppression

actions. Part of the HazRed Project involved commercial thinning and prescribed burning in portions of existing fuelbreak segments that were logged

10 years earlier. However, an abundance of smaller, submerchantible trees

had been left behind from the timber sales, and manzanita brush had rapidly

grown in the opened canopies. These small trees and brush made these sites

largely ineffective as fuelbreaks. Because the Forest had failed to adequately

maintain existing 20-year-old fuelbreaks, this did not give the community much

assurance that the proposed new fuelbreaks would be maintained for the next

200 years—the timeframe that the Forest had used to analyze the effects of

the fuelbreaks in protecting the watershed from future large-scale “catastrophic”

fires. Additionally, the use of chemical, mechanical, manual, and prescribed

burning methods for fuelbreak maintenance cause their own cumulative impacts,

which needed to be analyzed along with the effects of fuelbreak construction.

The Ashland Watershed Protection Project

Draft Environmental Impact Statement

The Regional Office instructed the decisionmaker to withdraw the DN and

issue an additional 30 day comment period for the revised Final EA. At the

Ranger’s own discretion, however, she decided to conduct a more extensive

environmental analysis and develop a new Draft EIS. The HazRed Timber

Sale Project was renamed the Ashland Watershed Protection Project (AWPP).

The purpose and need for the AWPP was to provide high quality drinking

water and maintain large areas of late-successional habitat by creating a “fire

resilient landscape relatively resistant to large-scale high severity wildfire” [Draft

EIS; S-1] (USDA FS 1999).

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The Draft EIS included a range of four action alternatives (instead of only

two that had been in the HazRed EA). Alternative One was the No Action

alternative required as the baseline for comparing the action alternatives. The

objective of Alternative Two was to protect late-successional structure and

treat understory vegetation and surface fuels using prescribed underburning

as the only treatment method. It sought to replicate, to the extent possible,

the historical fire cycles for the Project area and restore historical vegetation

conditions in the watershed. Alternative Three stressed protection of soils and

site productivity by using manual treatments (e.g., cutting with chainsaws and

handtools ) and “swamper burning” (continuously feeding material into small

piles about 4-6 in feet diameter) to selectively remove shrubs, small trees up

to 8 inches DBH, and jackpots of dead surface fuels, rather than mechanical

treatments with fellerbunchers and skidders. Cutting with chainsaws and hand

tools only and small pile and swamper burning would avoid the impacts on

soils from both heavy equipment logging and broadcast prescribed burning.

Alternative Four would try to minimize changes to late-successional forest

structure while reducing fire hazard. It proposed using a combination of treatment methods including mechanically removing trees up to 17 inches DBH,

manual treatments, and prescribed underburning, but would not construct or

expand shaded fuelbreaks. Slash and fuels would be burned by a variety of

methods and possibly chipped and hauled away for biomass. Alternative Five,

the preferred action, was essentially the proposed action in the HazRed Project,

and its goal was to maximize fire hazard reduction using all of the above

treatment methods and maintaining, expanding, and constructing shaded

fuelbreaks, with no diameter limit on the trees slated for mechanical removal.

The AWPP was a significant improvement over HazRed on some but not

all issues. As an EIS, the AWPP did provide a much wider range of alternatives, including two alternatives using noncommercial methods. However,

conservationists were critical of splitting up the manual treatments and prescribed underburning into two separate proposals. They believed that combining

those methods would have successfully reduced surface and ladder fuel loads,

raised the crown base height, and increased the average stem diameter in ways that

would have significantly reduced the risk of crownfire initiation. This would have

reduced the need to extract large trees in order to reduce crownfire propagation.

The Draft EIS also disclosed the existence of the Federal Wildland Fire

Policy and its nine guiding principles, but the Ranger dropped from consideration an alternative that would have utilized Wildland Fire Use for Resource

Benefits (WFURBs) precisely because the Forest’s existing suppression-based

fire-exclusion-oriented FMP did not allow for WFURBs in the watershed. It

was assumed throughout the analysis that fire suppression would occur, and

the Draft EIS even provided some crude estimates of the potential costs of

future suppression for each alternative. Unfortunately, the Draft EIS did not

analyze the potential environmental impacts of suppression within the

fuelbreaks. Later, in the Final EIS, the agency would argue that a Forest Plan

Amendment or Revision would be necessary in order to fully implement the

Federal Fire Policy and utilize WFURBs [Final EIS; App. I-10] (USDA FS 2001a).

The Ashland Watershed Stewardship Alliance

Prior to the release of the Draft EIS, the District Ranger took the initiative

to reduce some of the tension that had flared up in the community over the

original HazRed Project and seek active citizen involvement in the AWPP.

She contacted the Peace House, a local nonprofit organization affiliated with

the National Fellowship of Reconciliation, and asked them to organize a “community dialogue meeting” in February 1999. As a neutral ground with a large

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amount of credibility in the community, the Peace House brought together

the Ranger and environmental activists for face-to-face discussions about the

forthcoming AWPP. From the original small gatherings, the group decided to

meet regularly and expand the base of participants. Representatives of the

City of Ashland, business owners, forest workers, and community organizers

met twice a month from March through August when the Draft EIS was

released. In September, the group named themselves the Ashland Watershed

Stewardship Alliance (“the Alliance”). They began to meet twice weekly and

set up four subcommittees that reported to the larger group. About 40 people

actively participated in the Alliance meetings, with some gatherings attracting

over 100 people sitting in a large circle at the Peace House to discuss competing and complementary visions on how to protect and restore the Watershed.

Although the District Ranger did not directly participate after the first few

meetings, the community recognized that it was her initiative and tacit

ongoing support that kept the Alliance meeting regularly and working for

a new, constructive, collaborative relationship between the agency and community.

The Stewardship Alliance was comprised of highly experienced and skilled

people, including Headwater’s staff, a member of the Society of American

Foresters, the Ashland City Forester, a retired Forest Service economist, as

well as other credible scientists, foresters, and environmentalists residing in

the community. The Alliance produced a 95 page proposal that was submitted

on the last official day for comments on the Draft EIS. The preamble to this

document is noteworthy for the spirit of collaboration it conveys:

“We, involved citizens of the Ashland Watershed, look to deal constructively with conflict and promote a collaborative relationship

between the Forest Service and the people of this community. As

neighbors within this forest, we share a common interest to begin

the work necessary to mitigate the risks of wildland fire within the

watershed, restoring a forest ecosystem which will be resilient to

periodic natural fire events. We seek to accomplish this goal while

maintaining the ecological, social, aesthetic, spiritual, economic and

educational qualities which the people of this region value in these

forests...Working with the Ashland Ranger District we hope to

develop alternatives which can cover the costs, while building a lasting and mutually beneficial relationship with the agency—one which

utilizes local expertise, folds local values into the planning process,

and builds a culture of long-term stewardship between the citizens

of this community and the land” (Ashland Watershed Stewardship

Alliance 1999).

The Alliance’s Proposal presented a number of ecological, social, and economic goals and principles that they wished to be applied toward development

of a new alternative for the Final EIS. It is beyond the scope of this paper to

present the complete list (Ashland Watershed Stewardship Alliance 1999),

but the following items are worth emphasizing because they are increasingly

being requested by conservationists participating in fire and fuels related Project

proposals. Under the category of Ecological Goals and Principles: 1) Focus

fire hazard reduction activities primarily on reducing the fuels from the brush

and smaller understory trees that have increased above natural densities due

to fire suppression; and 2) Accomplish different aspects of the project in a

sequence that allows for non-controversial treatments to proceed as soon as

possible, so that lessons can be learned and applied later.

Under the category of Social Goals and Principles: 1) Develop and nurture

the shared responsibility of the community for the stewardship of the Ashland

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watershed (including planning, funding, implementation, and monitoring);

and 2) Establish a process encouraging participation by all interested parties

in an open and transparent manner that leads to understanding and trust.

Finally, under the category of Economic Goals and Principles: 1) Base the

decisions for fire reduction work on sound ecological guidelines and not on

the extraction of commercial material to meet Project funding needs; and 2)

Work with the U.S. Forest Service in developing new funding structures for

the fire hazard reduction project and if needed to secure additional funding

sources for the work (Ashland Watershed Stewardship Alliance 1999).

Among a number of technical items in the Alliance’s Proposal, they recommended a fire management strategy of area-wide vegetation treatments instead

of shaded fuelbreaks; preferred the use of manual pre-treatments with prescribed underburning instead of mechanical thinning in order to better conserve

soil and vegetation on geologically unstable slopes; demanded an active monitoring plan with secured funding; and desired a phased implementation of

project activities over time. The group felt that it was important to proceed

immediately on the activities that were non-controversial and enjoyed broad

community support. This meant the first phase of the Project would utilize

manual treatments to cut brush and small trees under 8 inches DBH in the

interface areas directly bordering residential sites. The second phase would

then be to combine manual treatments with prescribed underburning. The

third and most controversial phase of the Project would be to do maintenance

on the existing fuelbreaks, which if necessary could involve removing some

large trees. The fourth phase would be to develop a comprehensive long-term

fire restoration plan for the whole watershed, based on the lessons learned in

the prior three phases. The Alliance gained endorsements for their alternative

proposal from all of the area’s environmental organizations, a wide spectrum

of community organizations and private citizens, and most importantly, from

the Ashland Mayor and City Council who gently reminded the Forest Service

of their 70-year-old Cooperative Agreement regulating management activities in the watershed.

As mentioned, the Alliance expressed the desire to work with the Forest

Service to develop innovative, non-traditional funding mechanisms such as

service contracts and special use permits to get the needed work done. For

example, the Alliance offered to create its own non-profit, bonded general

contracting arm to bid on high priority, low revenue producing units that

would not likely attract traditional contractors. The non-profit could offer

below-market bids on service contracts, and then accept donations from local

citizens and businesses to help cover expenses. These and other initiatives

represented sincere desires by the Alliance and the local community to work

with the Forest Service to implement widely supported hazard reduction activities in the watershed while avoiding the need for a controversial and

potentially divisive commercial timber sale.

The Ranger decided to extend the initial 45 day comment period on the

Draft EIS by an additional 30 days. She conducted a public “learning meeting” on September 1, 1999, to make herself and her staff available for questions

about the Draft and address the public’s interests and concerns. Even more

impressively, she allowed citizens to check out keys to the Forest Service’s

locked gates in order to access portions of the Project area that were normally

restricted. Of the 39 comment letters received (several of them contained the

signatures of multiple individuals and/or groups), 21 letters expressed support for the Alliance’s vision of proactive community involvement in all of the

fire hazard reduction activities stages (planning, implementing, and monitoring) in the Watershed and WUI zone [Final EIS; App. I-3] (USDA FS 2001a).

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236 USDA Forest Service Proceedings RMRS-P-29. 2003.

Final Environmental Impact Statement

The Final EIS was issued on January 2, 2001, and included the important

addition of a new alternative developed in response to public comments. Learning from the past mistake, the decisionmaker provided a 30 day comment

period on the FEIS. The Stewardship Alliance was disappointed that the

decisionmaker did not agree with them on some issues; nevertheless, the Final

EIS represented a significant improvement over the Draft, with five times

more acres to be treated with noncommercial methods, much less commercial

thinning, and most importantly it included a new preferred alternative that

was inspired by the Alliance’s proposal.

Alternative Six, the agency’s new preferred alternative, addressed the controversies surrounding overstory tree removal and shaded fuelbreak constructed

by implementing a phased schedule of fuel treatments, beginning with surface

fuel reduction in the WUI zone, prior to treatments to maintain existing

ridgeline fuelbreaks in the interior of the watershed. The alternative also called

for implementation of manual surface and understory fuels reduction for the

first three years of the Project. For the next 3-5 years, mechanical thinning

would occur, followed by prescribed underburning, all in the WUI zone. No

new shaded fuelbreaks would be constructed, and although it would still

remove overstory trees, it would not follow the same canopy spacing prescriptions (e.g., 20-60 feet spacing between individual tree crowns) proposed in

the previous proposed actions in HazRed and the AWPP. The whole process

of reducing fire hazards on 1,549 acres was estimated to take 8-12 years to

complete, with implementation and effectiveness monitoring to be conducted

at each stage. Alternative Six was analyzed as the most costly to implement,

but the Alliance felt that the more costly, the better, since they believed that

restoration activities should be funded through appropriated budgets such as

the Hazardous Fuel Reduction fund within the National Fire Plan, and not

have to depend on revenues derived from commodity timber extraction.

Record of Decision

The Record of Decision (ROD) for the AWPP was issued on May 25, 2001—

fully five years after the initial HazRed Project was first proposed. The Ranger

noted that it had been “a protracted agency process with stunning citizen

involvement” [ROD; p.2] (USDA FS 2001b). She commended that “the Forest

Service is fortunate to have membership with a community so committed to

building its capacity to leverage diversity and resources to address difficult,

and sometimes seemingly irresolvable, natural resource and social issues” [ROD;

p.2] (USDA FS 2001b). It is significant that the Ranger considered herself

and the District staff to be members of the community. A year earlier (June

2000) she was fired by the Rogue River Forest Supervisor in his last official act

before retiring from the Forest Service. The alleged reason for her dismissal

was her “incompetent management style.” Community members believed,

however, that she was fired because her management style emphasized active

involvement and community collaboration, in contrast to the agency’s traditional technocratic style. Dozens of local citizens called the Regional and

Washington Offices of the Forest Service to complain. The result: two days

after she had been dismissed she was fully reinstated as the Ashland District

Ranger, and the community was elated. Clearly the Ranger considered herself, and was considered by local residents, to be a member of the community.

Acknowledging that the removal and sale of large trees was the most contentious aspect of the Project and provoked “vehement resistance” from

citizens, the Ranger selected a modified Alternative Six that deferred for

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several years the commercial logging planned along ridgeline fuelbreaks, and

imposed a diameter limit of 17 inches DBH for trees to be commercially thinned

within the WUI zone. Citizens had long pushed for this diameter cap based

on the Mt. Ashland LSR Assessment, which disclosed that trees larger than 17

inches DBH contribute to habitat quality for northern spotted owls. The ROD

also withdrew the new road reconstruction and eliminated a proposed helispot

that was the site of an old-growth ponderosa pine stand containing some of

the biggest trees originally proposed for cutting. If and when commercial

thinning for fuelbreaks might occur in the future, eliminating that new road

and helispot will result in longer helicopter flights, ultimately making the logging more expensive to implement. Regardless, the Ranger justified her decision

by saying that the citizen’s and the City’s environmental concerns far outweighed the agency’s desires for least-cost project implementation and

maximum fuels reduction effectiveness.

Finally, the Ranger acknowledged the benefits of community collaboration

and outlined several opportunities for citizen volunteer participation and continued involvement in the Project’s implementation. For example, the Ranger

offered to help organize “volunteer days for community participation” in pretreatment data collection and post-treatment monitoring to train citizen

volunteers in manual fire hazard reduction techniques and to let them actually

assume responsibility for treating selected units. The Ranger’s appeal for citizen volunteers included helping to unmark all the previously marked trees

within units dropped from mechanical treatments. In addition to helping to

train and organize citizen volunteers, the Forest also promised to implement

the Project with a variety of methods that utilize local labor resources, including the awarding of “best value” service contracts.

Although the ROD had successfully addressed the majority of the conservation community’s concerns, the Klamath-Siskiyou Wildlands Center (KSWC)

did appeal the portion of the Project that would have used commercial thinning in high risk landslide areas. In an informal disposition meeting, the Ranger

agreed to drop all units in those landslide-prone areas, and thus reduced the

total logging acreage down to 116 acres—fully one-fourth the acreage of the

original HazRed proposal. The KSWC dropped their appeal, and the Project

has been carried forward with the active support and involvement of the KSWC.

The Ranger has gained the trust of the local conservation community who

feels that under her leadership the Project has been transformed from essentially a pre-suppression timber sale into a genuine fuels reduction for fire

restoration project.

Conclusion

The Ashland Ranger District has begun implementing the AWPP, and it

has received priority National Fire Plan funding in Region 6 precisely because

the Project had gained extensive community involvement and support. Both

the District Ranger and the local conservation community should be credited

with the willingness to communicate and collaborate in creating a management project that is both environmentally sound and socially acceptable. The

fact that the Ranger did not come from a traditional forestry background, but

instead, had an educational background in communication and leadership may

account for her remarkable success in listening and learning from the community and leading her staff to develop a truly innovative Project. She is continuing

to serve as a facilitator of community-based stewardship, working on a proposal with the City of Ashland and Southern Oregon University to create the

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238 USDA Forest Service Proceedings RMRS-P-29. 2003.

“Southern Oregon Institute for Watershed and Citizenship Studies” to help

increase agency and community capacity for ecological restoration of public

lands (Duffy 2001).

The Project’s manual cutting and burning treatments will be expensive, but

the National Fire Plan funding for Hazardous Fuels Reduction treatments

comes at the perfect time for these noncommercial treatments. The Ranger,

the Stewardship Alliance, and the City have also pledged to leverage the funding by organizing groups of volunteers to help implement the Project on the

ground. A subgroup of Americorps called “REALcorps” (Regional Ecosystem Applied Learning) is organizing community work parties to do hand-piling

of fuels, and the nonprofit Lomakatsi Restoration Project has received grants

from the National Fire Plan fund to train local citizens in hazardous fuels

reduction and restoration techniques. Local groups of students and Boy Scouts

are also getting involved in cutting brush in the WUI zone. The Forest Service is experimenting with different kinds of service contracts to allow

community groups to design their own manual hazardous fuels reduction prescriptions on three acre parcels of land. In sum, the community is taking several

initiatives to implement the Project, and the Forest Service is playing a supportive role by providing technical assistance. This represents a most novel

approach to agency-community collaboration: teaming up the expertise and

authority of the agency with the volunteer labor power of the community to

get the work done for mutual benefit.

The legacy of the Ashland Watershed Protection Project is a matter of perspective. To some Forest Service officials, the Project’s prolonged analysis,

successful appeals, and deferral of the timber sale represents a humbling

erosion of managerial power. To other agency employees, the Project represents an important breakthrough in gaining the consensus of “hardcore”

conservationists for active management in a forest reserve. To the local community, the Project symbolizes a new social solidarity and unprecedented

opportunity to assume some civic responsibility for stewardship over the

public lands they hold dear. The phased implementation of the Project, starting with noncommercial manual and prescribed burning treatments first,

followed by implementation and effectiveness monitoring, and then later

mechanical treatments possibly including commercial logging, created a successful solution to overcoming controversy and “analysis paralysis.”

According to a local resident who actively participated in the NEPA process

and helped develop the Stewardship Alliance’s proposal, “Despite the prolonged and difficult process, the AWPP is evidence that NEPA actually works.

Although it is time and energy consumptive, if followed faithfully the NEPA

process produces projects that can unite instead of divide communities”

(Lininger, personal communication). In this regard, the Ashland Watershed

Protection Project offers a working model for agencies and communities to

move forward together on implementing fuels reduction and forest restoration projects throughout the National Forest System.

References

Ashland Watershed Stewardship Alliance. 1999. Draft comment and proposal for the

Ashland Ranger District and interested citizens in response to the Ashland watershed

protection project draft environmental impact statement. November 1999.

Cohen, Jack. 1999. Reducing the wildland fire threat to homes: where and how

much? Proceedings of the symposium on fire economics, planning, and policy:

Ingalsbee From Analysis Paralysis to Agency-Community Collaboration in Fuels Reduction for Fire Restoration: A Success Story
USDA Forest Service Proceedings RMRS-P-29. 2003. 239

bottom lines. Gen. Tech. Rep. PSW-GTR-173. U.S. Department of Agriculture,

Forest Service, Pacific Southwest Research Station: 189-195.

Duffy, Linda. 2001. Transformation of a ranger district? The Stewardship Chronicle:

the newsletter of the ecosystem workforce program. Volume 3, Number 1. Spring.

Lininger, Jay. 2002. Personal interview.

U.S. Department of Agriculture Forest Service. 1996. Late-successional reserve

assessment, Mt. Ashland late-successional reserve (#RO-248). Pacific Northwest

Region (R-6), Ashland Ranger District, Rogue River National Forest; Pacific

Southwest Region (R-5), Oak Knoll Ranger District, Klamath National Forest.

June 1996.

U.S. Department of Agriculture Forest Service. 1997. Draft environmental assessment,

Ashland interface fire hazard reduction (HazRed) project. Ashland Ranger District,

Rogue River National Forest. February 1997.

U.S. Department of Agriculture Forest Service. 1998. Final environmental assessment,

decision notice, and finding of no significant impact, Ashland interface fire hazard

reduction (HazRed) project. Ashland Ranger District, Rogue River

National Forest. March 1998.

U.S. Department of Agriculture Forest Service. 1999. Draft environmental impact

statement, Ashland watershed protection project. Ashland Ranger District, Rogue

River National Forest. August 1999.

U.S. Department of Agriculture Forest Service. 2001a. Final environmental impact

statement, Ashland watershed protection project. Ashland Ranger District, Rogue

River National Forest. January 2001.

U.S. Department of Agriculture Forest Service. 2001b. Record of decision, Ashland

watershed protection project. Ashland Ranger District, Rogue River National

Forest. May 2001.

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240 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 241

Hazardous Fuel Reduction in the

Blue Mountains: Public Attitudes and Opinions

Eric Toman1 and Bruce Shindler1

Abstract—Resource managers in the Blue Mountains region of eastern Oregon and

Washington are utilizing prescribed fire and mechanized thinning treatments to

reduce hazardous fuel loads and restore forest health. This paper uses panel data

from a mail survey administered to the same individuals in 1996 and 2000 to measure change in public attitudes and opinions about fire management programs.

Respondents are knowledgeable about, and supportive of, prescribed fire and thinning practices; prefer interactive over uni-directional education programs; and desire

a role in management decision-making. While findings were generally similar throughout the study period, significant changes suggest a declining relationship between

the Forest Service and Blue Mountains residents.

Introduction

Forests in the Blue Mountains region of Oregon and Washington are

threatened by drought, insect outbreaks, and the risk of catastrophic

wildfire (Mutch et al. 1993, Tanaka et al. 1995). Resource managers on the

National Forests within the region are utilizing multiple strategies to reduce

hazardous fuels and restore forest health. Two of the most frequently used

methods are prescribed fire and mechanized thinning treatments. Research

demonstrates that a large-scale application of these treatments can address the

principal causes of declining forest health and increased fire risks and serve to

increase biological diversity, improve plant communities, reduce the number

of invasive species, and ultimately create more natural forest conditions (Mutch

et al. 1993).

The purpose of this study is to improve our understanding of the factors

that contribute to public acceptance of prescribed fire and mechanized thinning in the Blue Mountains area. Specific study objectives were to a) compare

the current research findings with the 1996 study to describe changes in public attitudes and behaviors, b) identify levels of support for fuel reduction

activities, c) identify citizens’ information needs, preferred forms of information exchange, and which delivery systems are most effective, and d) assess

interactions between the public and the Forest Service.

Management Context

Declining forest health is not unique to the Blue Mountains but is evident

throughout forests of the western United States. Poor forest conditions have

greatly increased the risk of catastrophic wildfire and the fires of 2000 brought

national attention to this situation. Interagency planning efforts have resulted

1Department of Forest Resources,

Oregon State University, Corvallis, OR.
242 USDA Forest Service Proceedings RMRS-P-29. 2003.

in a national strategy to reduce wildfire risks while restoring ecosystem health

and protecting communities through fuel reduction practices. Citizen support is a basic requirement to project implementation and long-term success.

The public has legitimate concerns about these activities following decades of

fire suppression activities, fire exclusion education from forest management

agencies, and exposure to high-profile escaped prescribed fires such as the

Cerro Grand Fire in Los Alamos, New Mexico, during spring 2000. Current

agency educational programs nationwide, and within the Blue Mountains

region particularly, attempt to address these concerns. Fuel reduction projects

and information programs on the Wallowa-Whitman, Umatilla, and Malheur

National Forests provide an opportunity to examine citizen perspectives on

the legitimacy of these practices and the effectiveness of informational messages.

Although public awareness of prescribed fire is growing, this use of fire still

seems contradictory to the practices that many have come to expect from

forest agencies (Beebe and Omi 1993, Lee 1987). Public concerns with prescribed fire typically focus on risk (danger of escapes to public safety and

private property), aesthetics (potential loss of scenic quality and recreation

uses), health issues (the impact of smoke on air quality), ecological effects

(impacts to wildlife, vegetation, water quality), and economic impacts (loss of

valuable timber).

In spite of these concerns, public attitudes nationwide have been evolving

toward a greater acceptance of the use of fire in forest ecosystems, particularly

as agencies improve their communication strategies (e.g., Carpenter et al. 1986,

Loomis et al. 2001, Stankey 1976). This is the case in the Blue Mountains

where our 1996 study identified an association between increased support for

prescribed burning activities with increased citizen knowledge about the role

and uses of fire. Although other studies have also found associations between

factors such as gender, income, age, backcountry experience, and support levels (Carpenter et al. 1986, McCool and Stankey 1986), knowledge and

understanding of prescribed fire continue to be the most important contributors

to public attitudes about the practice.

Little research has been conducted on public perspectives of mechanized

thinning methods to reduce hazardous fuels. Concerns range from aesthetic

impacts and potential ecological effects of harvesting practices (Brunson and

Reiter 1996) to doubts about whether thinning treatments will result in a

sufficient quantity of marketable timber to offset increased operation costs

(Shindler and Collson 1998). Another important issue involves public trust in

our forest agencies to effectively implement mechanized thinning programs

on federal lands; in particular, citizens have reservations about how much

license managers will take in thinning forest stands. Overall, timber harvesting

is one of the most contentious issues in the highly charged sociopolitical environment in which our natural resource agencies operate. One of the dangers

for fire managers is that many citizens believe using thinning treatments to

reduce fuel loads is really just another way to continue harvesting or, in other

words, conduct “business as usual” (Stankey 1995).

Methods

Methods regularly employed in social science research provide a “snapshot” of a cross-section of the population at one specific point in time;

researchers then make inferences about existing conditions and circumstances.

Toman and Shindler Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opinions
USDA Forest Service Proceedings RMRS-P-29. 2003. 243

Although careful analysis of cross-sectional data can provide considerable

insight, there are limitations in our ability to understand ongoing processes

with data collected from a single reference point (Babbie 1995). To overcome

these limitations, longitudinal research designs provide for data collection and

analysis over time.

A particularly beneficial type of longitudinal research is a panel study that

involves evaluations of the same individuals using the same measures at different points in time. Panel data can provide a richer understanding of ongoing

processes and be used to identify general trends within the population of

interest. Responses from the individual study participants can be “paired,” or

linked, over the separate data collection points to allow identification of shifts

in individual attitudes and beliefs. Paired data typically reduce the variability

that could obscure small but significant differences between results (Devore

& Peck 1986).

Data Collection

The current study is a panel study (designed to replicate research conducted

in 1996 by Shindler and Reed). A new mail-back questionnaire was sent to all

1996 study participants in the summer and fall of 2000. The majority of questions were replicated to allow for comparison of responses between years;

additional questions were included to address current concerns, notably specific Forest Service information programs and citizen-agency interactions. When

appropriate, data from replicated questions (1996 and 2000) are compared

utilizing paired t-tests and significant differences in responses are noted.

In 2000, 455 of the 533 original respondents were located. Of these, 32

were removed from the sample (29 were deceased or unable to complete the

survey due to health reasons, and 3 had moved from the Blue Mountains

region). From the useable sample of 423 names, 323 respondents completed

questionnaires for a 76% adjusted response rate.

Findings

Public Knowledge, Information Sources, and

Forest Health Conditions

To help gauge citizen understanding about the use of fire and thinning, a

new line of inquiry was introduced in the 2000 questionnaire that engaged

respondents in a 15-item true/false quiz. Quiz statements and responses are

shown in table 1.

Overall, respondents were generally knowledgeable about both prescribed

fire and thinning; the average correct score across all questions was 70%. However, some public misperceptions seem noteworthy. About one-third (35%) of

the respondents did not know (answered incorrectly or not sure) about the

important role fire has played in shaping natural forests in the Blue Mountains. Almost half were either misinformed or not sure about the effects of

prescribed fire on small trees and understory vegetation, in promoting growth

of ponderosa pine, and in controlling noxious weeds — all key objectives for

the use of prescribed fire. Similarly, almost one-third (30%) did not know that

thinning could be used to encourage growth of ponderosa pine as research

has demonstrated (e.g., Cochran and Barrett 1993). Overall, participants

appeared significantly more knowledgeable about the effects of thinning than

about prescribed fire.

Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opinions Toman and Shindler
244 USDA Forest Service Proceedings RMRS-P-29. 2003.

Knowledge and information play an important role in forming support for

management practices. In peoples’ everyday lives, there are many different

places where citizens might obtain information about natural resource issues.

Using a 4-point scale (none, slight, moderate, high) respondents were asked

to rate the usefulness of nine likely sources of information about forest management in the Blue Mountains (figure 1).

Newspapers/magazines and friends or relatives continued to be the most

useful sources and were the only ones to receive a moderate to high rating by

a majority of respondents in both 1996 and 2000. Of particular interest are

ratings for timber groups and the Forest Service; these are the only sources to

receive significantly different ratings during the study period. The usefulness

rating of timber groups rose (39% to 50%), while opinions of the Forest

Service as a useful information source fell from 60% to 48%. This finding may

suggest that the traditional communication formats utilized by the Forest

Service (i.e. brochures, public meetings, exhibits) are less effective or that

people doubt the credibility of government provided information (Shindler et

al. 1996). Overall, the lowest ratings were for radio, environmental groups,

and the internet.

To probe this area more thoroughly in 2000, additional questions focused

on specific Forest Service information programs. Respondent familiarity as

well as program components such as ease of understanding, convenience, and

trustworthiness factored into usefulness ratings; however, only the percentage

of moderately or highly useful ratings from respondents familiar with the program are displayed in figure 2. For a more complete analysis of the ratings of

agency information programs see Shindler and Toman 2002.

Smokey Bear, elementary school educational programs, conversations with

agency personnel, interpretive information, and guided field trips received the

highest usefulness ratings. Television messages, newsletters, brochures, exhibits at fairs, and public meetings were moderately useful. Alternatively, few people

Toman and Shindler Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opinions

Table 1—Citizen knowledge about the effects of fire and thinning in Blue Mountain forests.Answer generally considered correct is in

parentheses and percent answering correctly is indicated in bold.

Generally

 Not

 - - - - - - - - - - - - - - - - - - - - - - Fire - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - True False sure

a. Fires have played a significant role in shaping natural forests in the Blue Mountains. (True) 66% 13% 22%

b. Prescribed fires make additional minerals and nutrients available for plants and trees. (True) 67% 12% 21%

c. Prescribed fires cause the immediate death of the majority of animals in the burned area. (False) 7% 80% 13%

d. Prescribed fires result in the death of the majority of large, established trees in the burned area. (False) 15% 74% 11%

e. Prescribed fires promote the growth of plants that serve as food for deer and elk. (True) 84% 9% 7%

f. Prescribed fires kill most of the small, young trees and vegetation beneath the forest canopy. (True) 51% 30% 19%

g. Prescribed fires encourage tree growth in ponderosa pine forests. (True) 56% 10% 34%

h. Prescribed fire is effective in controlling noxious weeds. (True) 52% 24% 24%

 - - - - - - - - - - - - - - - - - - - - Thinning - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

i. Selective thinning can be effective in controlling outbreaks of insects and disease. (True) 78% 7% 15%

j. Selective thinning reduces competition for minerals and nutrients on crowded sites. (True) 80% 7% 13%

k. Selective thinning mimics natural conditions by providing openings in the forest canopy. (True) 74% 9% 17%

l. Selective thinning causes the immediate death of the majority of animals in the thinned area. (False) 2% 89% 9%

m. Selective thinning encourages tree growth in ponderosa pine forests. (True) 70% 6% 24%

n. Selective thinning results in decreased habitat for deer and elk. (False) 13% 77% 11%

o. Selective thinning results in the death of the majority of the remaining trees on the site. (False) 3% 90% 8%
USDA Forest Service Proceedings RMRS-P-29. 2003. 245

26%

33%

33%

47%

53%

54%

55%

76%

46%

62%

66%

68%

70%

0% 20% 40% 60% 80% 100%

Forest Service internet web pages

Informational videos

Environmental impact statements

Forest Service public meetings

Exhibits at State/County fairs

Prescribed fire brochures

Regular newsletters

Television messages

Guided field trip to forests

Interpretative information

Conversations with agency personnel

Elementary school educational programs

Smokey Bear message

Interactive

Uni-Directional

Figure 2—Usefulness of Forest Service information and outreach in the Blue Mountains. Data reflect

percentage of citizens who rate usefulness as moderate or high on a 4-point scale (none, slight, moderate,

high).

13%

24%

35%

42%

46%

48%

50%

56%

72%

8%

17%

40%

38%

56%

60%

39%

60%

72%

0% 20% 40% 60% 80% 100%

Internet

Environmental Groups

Radio Station

University Faculty

Television Programs

Forest Service Programs*

Timber Groups*

Friends/Relatives

Newspapers/Magazines

1996

2000

Figure 1—Useful information sources about forest management in the Blue Mountains. Data

reflect percentage of citizens who rate usefulness as moderate or high on a 4-point scale

(none, slight, moderate, high). *1996 and 2000 responses are significantly different at p £ .01

Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opinions Toman and Shindler
246 USDA Forest Service Proceedings RMRS-P-29. 2003.

found Environmental Impact Statements, informational videos, and Forest

Service internet web pages to be useful.

The outreach programs have been further divided into interactive and unidirectional formats. Interactive programs are those that provide for either

personal contact with agency representatives or on-the-ground learning experiences. Uni-directional programs are those that typically involve a one-way

flow of communication from the agency to the public. While web pages have

the capability to provide a form of virtual interaction, they are included with

the one-way messages because none of the Forest Service websites within the

Blue Mountains offered an interactive option at the time of this study.

Four of the five most highly rated programs were interactive—elementary

school programs, conversations with agency personnel, interpretative centers,

and guided field trips—indicating greater dividends may be achieved from

this form of outreach. Of the interactive programs, only Forest Service public

meetings failed to resonate with a majority of the respondents. Of the unidirectional programs, four—Smokey Bear, television messages, newsletters,

and prescribed fire brochures—were useful to a majority of respondents.

Given that one of the stated goals for the use of prescribed fire and mechanized thinning treatments is the restoration of healthy forest conditions,

respondents were asked to indicate their perception of the condition of Blue

Mountain forests. Opinions about forest health conditions have improved

significantly since 1996 (figure 3). While only a few respondents considered

conditions to be “very healthy” in either year, responses generally shifted from

the lower end (unhealthy) toward the “somewhat healthy” category.

1% 2%

22%

47%

22%

40% 37%

11%

0%

20%

40%

60%

80%

100%

Very Unhealthy Somewhat

Unhealthy

Somewhat

Healthy

Very Healthy

1996

2000

Figure 3—Overall condition of

forests in the Blue Mountains. “Don’t

know” responses omitted. 1996 and

2000 responses significantly different

at p £ .01.

Citizen-Agency Interactions

The ability for agencies to interact effectively with local publics is important

to successful implementation of management activities (Shindler et al. 2002).

Table 2 shows responses about citizens’ experiences with the Forest Service

from both surveys and responses to four new questions asked only in 2000.

Opinions were mixed about these interactions; however, if viewed as a report

card, the scores overall were not particularly good.

More specifically, the data show there were several changes since 1996 in

respondent assessments of their interactions with the Forest Service. Although

agreement was low to begin with, significantly fewer respondents agreed in

2000 that federal agencies use public input to shape management decisions.

Toman and Shindler Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions
USDA Forest Service Proceedings RMRS-P-29. 2003. 247

Given this level of response, it is curious that the number of respondents who

agreed that managers usually create plans without input from local communities has decreased, although almost half still believe this to be the case. These

two responses seem contradictory but could indicate that while more citizens

recognize the Forest Service does solicit public comments, those same citizens do not believe these comments are reflected in management plans. Also,

fewer respondents, just 27%, believed the Forest Service does a good job of

providing information about its management activities. As in 1996, a majority

of respondents agreed that our federal forest management systems need major

changes and nearly 50% felt the Forest Service should provide a stronger leadership role.

Responses to new statements added on the 2000 questionnaire provide

increased cause for concern. Few people viewed Forest Service actions as building trust and cooperation with citizens; in fact, a majority disagreed with this

statement. The next two items seem to shed light on this finding. Frustration

over national level politics and external influences on managing local forests

runs high; about two-thirds indicated they trust local Forest Service personnel, but feel government at the national level hinders these individuals from

doing their job. Participants were split in their agreement of whether the

Forest Service educates communities about the benefits and costs of proposed

plans.

A substantial number of people chose the neutral response for most statements. Since no “don’t know” category was provided, it may be safe to assume

that many of the neutral responses were from people who had no basis for

judgment about these issues. This situation usually indicates an opportunity

to reach out to a segment of the public and positively influence how these

individuals come to view the agency.

Table 2—Experiences and interactions with forest management agencies.a

Significance

Agree Neutral Disagree levelb

Agencies like the Forest Service are open to public input 1996 41% 25% 34% £ .01

and use it to shape forest management decisions. 2000 31% 27% 42%

Forest managers usually create plans without input from 1996 55% 23% 22% £ .01

local communities surrounding National Forests. 2000 46% 24% 30%

The Forest Service does a good job of providing information 1996 33% 33% 35% £ .05

about its management activities. 2000 27% 29% 43%

Our federal forest management systems need major changes, 1996 59% 24% 18% NS

not just minor adjustments. 2000 62% 23% 15%

The Forest Service should provide a stronger leadership role. 1996 54% 32% 14% NS

2000 49% 35% 17%

Federal forest managers build trust and cooperation with 2000 23% 25% 52% N/A

citizens so that people will feel that the agency is acting

in their best interest.

I trust the local Blue Mountains Forest Service staff, 2000 65% 19% 16% N/A

but I don’t trust government at the national level to

let them do their job.

Local Forest Service staff are prohibited from doing their job 2000 68% 24% 8% N/A

because of national restrictions and regulations.

The Forest Service contributes to public knowledge by 2000 33% 37% 30% N/A

educating communities about potential benefits and costs

of proposed plans.

a Responses on a 5-point scale from strongly agree to strongly disagree with a neutral midpoint.

b NS = Not Significant, N/A = question not asked in 1996

Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions Toman and Shindler
248 USDA Forest Service Proceedings RMRS-P-29. 2003.

Public Attitudes About Prescribed Fire and Mechanized

Thinning

Given the many different terms and definitions associated with prescribed

fire and thinning activities, care was taken to clearly explain the definition of

each practice as used in the questionnaire. The use of the term prescribed fire

was limited to management-ignited prescribed fire. Mechanized thinning treatments were defined to include the removal of down logs or standing dead and

dying trees less than 15 inches in diameter. Overall support for the use of

prescribed fire and mechanized thinning remained similar to 1996 (tables 3

and 4). Most respondents supported some use of prescribed fire in the Blue

Mountains; 39% believed the Forest Service should have full discretion for its

use, while an additional 50% felt the agency should use prescribed fire only in

carefully selected areas—an apt description of current agency fire policy. Support was significantly higher for mechanized thinning; 97% of respondents

supported some level of thinning with more than two-thirds giving the agency

full discretion for its use.

Table 3—Prescribed fire policies.a

The use of prescribed fire in the Blue Mountains... 1996 2000

. . . is a legitimate management tool that the Forest Service 44% 39%

should have the discretion to use for improving forest conditions.

. . . should be used sparingly by the Forest Service and only in 45% 50%

carefully selected areas.

. . . creates too many impacts and should not be considered 6% 7%

as a management alternative.

. . . is unnecessary and should not be utilized. 5% 4%

a

 No significant differences in responses between 1996 and 2000.

Table 4—Mechanized thinning policies.a

The use of mechanized selective thinning in the Blue Mountains. . . 1996 2000

. . . is a legitimate management tool that the Forest Service should 68% 69%

have the discretion to use for improving forest conditions.

. . . should be used sparingly by the Forest Service and only in 28% 28%

carefully selected areas.

. . . creates too many impacts and should not be considered as 2% 2%

a management alternative.

. . . is unnecessary and should not be used. 1% 1%

a

 No significant differences in responses between 1996 and 2000.

When asked to indicate their preference for treating the build up of dead

trees in the Blue Mountains, respondents clearly preferred a combined thinning and prescribed fire treatment (table 5). It is notable that very few

respondents (4%) believed that doing nothing about this problem is a responsible option.

Despite high levels of support, citizen trust in the Forest Service to implement responsible and effective fuel reduction programs appears low (table 6).

Regarding the use of prescribed fire, trust levels are not only low but decreased significantly during the study period (52% to 43%). While trust levels

Toman and Shindler Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions
USDA Forest Service Proceedings RMRS-P-29. 2003. 249

were slightly higher for the use of mechanized thinning, only a slim majority

(52%) expressed confidence in the agency. A substantial number of people

were still neutral regarding both practices, most likely reserving judgment

until they see the outcome of agency decisions.

Thus far, the most vocal opposition to the use of fire in the region has been

over increased smoke levels. Although temporary in nature, smoke has long

been recognized as a public concern associated with the use of prescribed fire,

especially because of adverse effects on human health and visibility in communities or along transportation corridors. In 1996 a substantial majority of

respondents indicated that smoke levels from prescribed fire were not a problem for their family, nor did they feel that prescribed fire use should be limited

because of potential health problems from smoke (table 7). Although the

majority of respondents still held similar views in 2000, significantly more

people now view smoke as a problem, and, as a consequence, fewer support

the use of prescribed fire. When dealing with smoke a simple majority in favor

of management practices is probably an insufficient level of public approval.

Smoke is a highly contentious issue that provides a rallying point for communities. Those adversely affected by smoke usually vocalize their concerns, a

point made by fire managers within the study area.

Table 6—Citizen trust in the Forest Service to implement fuel reduction programs.a

Significance

Agree Neutral Disagree level

I trust the Forest Service to implement 1996 52% 21% 25% £ .05

a responsible and effective prescribed 2000 43% 21% 34%

fire program.

I trust the Forest Service to implement 1996 59% 15% 24% NS

a responsible and effective mechanized 2000 52% 21% 26%

thinning program.

a

 Responses on a 5-point scale from strongly agree to strongly disagree with a neutral midpoint.

Table 5—Preferred treatment of the existing build up of dead trees in the Blue Mountains.

Agree

Selective thinning first, then follow with prescribed fire 75%

Use selective thinning only 20%

Use prescribed fire only 1%

Nothing, let nature take its course 4%

Table 7—Assessments of smoke.a

Significance

Agree Neutral Disagree level

In my area, smoke levels from fire 1996 76% 10% 14% £ .01

are not a problem for me or my family. 2000 61% 15% 24%

Prescribed fire should not be used 1996 12% 17% 71% £ .01

because of potential health problems 2000 14% 26% 61%

from smoke.

a

 Responses on a 5-point scale from strongly agree to strongly disagree with a neutral midpoint.

Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions Toman and Shindler
250 USDA Forest Service Proceedings RMRS-P-29. 2003.

Conclusions

Public acceptance is an essential element in virtually every resource management decision facing public agencies today. Problems such as fire

management and forest health, given the attendant risk and uncertainties

surrounding these issues, are particularly subject to public debate. Although

we have pointed out numerous areas of public support for fuel management

practices as well as several specific areas of concern, we recognize these are

complex issues. Problems frequently are embedded within larger issues and

connected to factors beyond the control of resource managers. Today, the

roles that agency personnel are being asked to play are much different than in

the past, when citizen participation was minimal and technical expertise was

foremost. In this new role, greater public acceptance will be achieved by being

aware of, and responsive to, the suite of intertwined ecological factors and

community circumstances affecting fuel management. Thus, our concluding

comments involve a set of three overarching themes, expressed as basic strategies that emerge from our analysis.

Capitalize on Existing Public Knowledge and Support for

Fuel Reduction

Primary results from this study indicate the presence of a knowledgeable

general public in the Blue Mountains, solid support for both prescribed fire

and mechanized thinning to reduce forest fuels, and an overall stability of

public attitudes throughout the study period. Collectively, these findings provide positive news for Forest Service programs. They also suggest that this

existing base of well-informed, supportive stakeholders could be a central

asset in building future management programs.

Study results also indicate that public understanding and support for treatments is not universal; trouble spots exist. Foremost is the provisional aspect

of public support, as evidenced by a drop in the number of people who think

the Forest Service can implement a responsible fuel management program.

Trust in the agency is central to this issue, but other factors are also likely to

apply. In the case of fire management, support is often dependent on the level

of uncertainty about outcomes and public understanding of the risks involved.

People want to know how serious conditions are, what will happen and when,

and who will be affected. Answers to these questions are not simple, nor are

they consistent across settings. But to be relevant to the public, fire management policies will need to be placed in a context that is important to them. To

the extent possible, managers will need to provide scenarios that depict what

changes in forest conditions will look like, how soon they could occur, and

help citizens understand what the consequences of changes will mean for forest ecosystems and surrounding communities.

Our analysis suggests that citizens in the Blue Mountains are cautiously

willing to allow these policies to proceed. As effective treatments are implemented and public awareness of these successes grows, so too will belief that

the Forest Service can be trusted to handle the risks associated with fire and

fuel management.

Focus on Relations With Citizens

The most troublesome finding from this study is the erosion of relations

between citizens and the Forest Service. As elsewhere, many of these feelings

Toman and Shindler Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions
USDA Forest Service Proceedings RMRS-P-29. 2003. 251

are attributable to the tension between policies set at the national level and

the need to create strategies for managing forests at the community level. But

it is not likely this circumstance explains all aspects of the declining relationship with the public in the Blue Mountains. Thus, two approaches seem

particularly useful. First, to the extent possible, agency personnel will need to

filter out the national debate and focus on what can be accomplished in local

forests. Programs will need to target local priorities. Second is the necessity to

increase opportunities for ordinary citizens and organized groups to have a

more meaningful role in planning and implementing fire management strategies. The common thread that runs through all aspects of forest management

is the importance of trustworthy relations among stakeholders. It really makes

little difference how meritorious a plan may be; nothing will be validated

unless the people involved trust one another.

The initial requirement for improving relations and building public trust is

an organizational commitment to multi-partner collaboration. This largely

depends on whether the leadership on Forests and ranger districts is serious

about genuine involvement of stakeholders and how well the actions of

personnel reflect this philosophy. Currently, most collaborative efforts and the

trust building process remain the job of individuals at the ranger district level

where relationships are established and face-to-face interactions can make a

difference for residents and their communities. The informal nature of these

situations is perhaps the most productive form of relationship building. But

this can only occur in a meaningful way when the agency promotes these ideas

and supports personnel in their outreach efforts.

Develop a Comprehensive Communication Strategy

Natural resource agencies often think their job is to develop information

and deliver it to the public. We still hear frustrated forestry professionals make

statements like, “If we could just educate people about the forest and inform

them about what we do, then they would understand and support us.” But

our tendency to confuse providing information with public understanding

and eventual support is a mistake. Although information and knowledge are

necessary elements of any public communication strategy, they are rarely sufficient to produce change in the way citizens respond to forest agencies. Public

judgments, including judgments of forest conditions and fuel reduction practices, are formed by a suite of factors beyond technical knowledge (Stankey

1996). Thus, the process of how people come to understand forest conditions

and support policies such as fuel reduction also needs to be an integral component of a communication strategy. A comprehensive public communication

strategy will not only focus on the types and content of the information that is

disseminated, but also on how and why it is communicated.

Regarding type and content, the data from this project can help organize

an approach for communication activities. Two basic levels of communication

exist, and each is useful depending on the purpose and intended coverage.

One is general information dispersal; this usually involves broad messages that

can be conveyed by traditional “bulk” formats such as newspapers, brochures

and public service announcements. These are typically for general public

consumption and, as such, provide few opportunities to target specific audiences. Because it is difficult to ensure that information is received and

understood, their effectiveness as an educational tool is limited. Data in this

study seem to reinforce this assessment; most forms of communication in this

category are uni-directional formats that received moderate to low level

usefulness ratings. Although the agency should continue to use these

Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions Toman and Shindler
252 USDA Forest Service Proceedings RMRS-P-29. 2003.

informational devices—they are often inexpensive, may be helpful in notifying

large audiences about upcoming meetings or proposed projects, and provide

some value collectively as a critical mass of information—outreach personnel

should not rely on these as primary tools for communicating with local publics.

The second level of communication is more focused in scope and usually

includes opportunities for interaction at the community or individual level. In

this category are the more highly rated activities such as school programs,

visitor centers, and guided field trips. Although these forms generally have

widespread acceptance as effective outreach devices, they are also highly

dependent on the communication abilities of agency personnel who plan and

implement them. For example, public meetings fall into this category but our

participants (as elsewhere) did not respond well to this format (Cortner et al.

1998). This seems to be a sign that the more traditional information-sharing

or scoping meetings, typically convened to meet NEPA requirements, should

be altered in favor of other meeting formats that provide for increased dialogue and two-way communication. But determining the most useful formats

is a job to be shared. From a practical standpoint, findings from this study can

be used by local personnel to engage communities about which level and forms

of information exchange are preferable.

The substantive content of information sources is also a primary consideration. Most content ideas are simple and straightforward—almost intuitive—but

neglecting them can be detrimental to communications. Research in forest

communities by Shindler and Neburka (1997) as well as Winter et al. (2002)

identified specific characteristics of good message content. These include:

• information where terms are defined for common use

• current, accurate, and understandable information that comes from a reliable source

• prescribed fire plans that specifically account for conditions such as weather,

proximity to homes and timing of events

• fire and thinning plans that provide mitigation measures to reduce impacts

on air quality, aesthetics, etc.

• contingency measures for escapes

• cost comparisons of various treatment alternatives

• specific details about who to contact for questions and concerns

Providing the opportunity for people to evaluate the range of information

about fuel management and the choices involved brings them much closer to

lending support for decisions. Most people like those in our study region are

capable of assessing the tradeoffs, including positive and negative consequences,

and welcome the chance to do so. When given a set of choices—even choices

that are limited or imperfect—citizens will often choose the lesser of the two

evils and accept it (Ehrenhalt 1994). The ability of fire management professionals to specify conditions and engage citizens in discussion about the nature

of the options is just as essential as providing objective, unbiased information.

Of course, this will mean that personnel must be forthcoming about the difficult decisions, including the uncertainty of outcomes associated with the use

of fire and thinning treatments.

Useful forums for discussion about forest conditions and fuel management

usually involve interactive exchanges, often in places where people can evaluate real-life scenarios prior to policy changes or broad scale implementation of

treatments. This study shows that people prefer settings where they can have

a more active, legitimate role—settings such as field visits to treatment sites to

Toman and Shindler Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions
USDA Forest Service Proceedings RMRS-P-29. 2003. 253

review alternatives or planning sessions where stakeholders are given consideration for their points of view and their suggestions are openly discussed and

evaluated.

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Peterson. 1993. Forest health in the Blue Mountains: A management strategy for

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Toman and Shindler Hazardous Fuel Reduction in the Blue Mountains: Public Attitudes and Opionions
USDA Forest Service Proceedings RMRS-P-29. 2003. 255

Fire, Fuel, and Restoration Priorities of the

Forest Conservation Community

Gregory H. Aplet1

I

n February 2001, forest activists from all over the country gathered in

Boulder, Colorado, to listen to presentations and engage in discussion

about the science and practice of forest restoration. A year in the making, the

result is a set of agreed-upon principles and criteria that form the foundation

of the conservation community’s stance on restoration and fuel treatment.

The document is organized around three core principles reflecting ecological,

economic, and community and workforce considerations. Paramount among

the principles are planning and adaptive management. With regard to fire, the

document acknowledges the importance of restoring natural fire regimes to

fire-dependent forests. To achieve this important goal, The Wilderness Society advocates a four-step approach: first, protect communities by addressing

home ignitability and adjacent fuels; second, complete fire management plans

on federal lands to allow Wildland Fire Use for Resource Benefit; third,

re-introduce prescribed fire where it is safe to do so; and fourth, where it is

not safe to burn, mechanically treat fuels to create a structure into which fire

can safely be reintroduced.

1 The Wilderness Society, Denver, CO.
256 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 257

Fire Regime Considerations
258 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 259

Key Issues in Fire Regime Research for Fuels

Management and Ecological Restoration

Thomas T. Veblen1

Abstract—The premise behind many projects aimed at wildfire hazard reduction

and ecological restoration in forests of the western United States is the idea that

unnatural fuel buildup has resulted from suppression of formerly frequent fires. This

premise and its implications need to be critically evaluated by conducting areaspecific research in the forest ecosystems targeted for fuels or ecological restoration

projects. Fire regime researchers need to acknowledge the limitations of fire history

methodology and avoid over-reliance on summary fire statistics such as mean fire

interval and rotation period. While fire regime research is vitally important for

informing decisions in the areas of wildfire hazard mitigation and ecological restoration,

there is much need for improving the way researchers communicate their results to

managers and the way managers use this information.

Introduction

The two major management themes of this conference are: 1) fuel treatments for the purpose of reducing fire hazard, and 2) ecological

restoration through a variety of management practices including prescribed

fire. The title and content of the conference might lead to the impression that

fire hazard reduction through fuel treatments and ecological restoration have

convergent objectives in all forest ecosystems in the western United States.

However, this implication needs to be explored on the basis of existing knowledge of historical fire regimes and forest conditions on a case by case basis for

different forest cover types and different locations. In some forest ecosystems

fire hazard reduction through fuels management may be achieved by restoring

historic fire regimes of frequent surface fires. However, in other forest ecosystems, historic fire regimes included widespread stand-replacing fires at long

intervals. In those systems, restoration of the historic fire regime will not

reduce the hazard to property and humans. This essay introduces a series of

papers on fire regimes by identifying some of the key issues and research challenges for fire regime research.

Political leaders and many resource management professionals often stress

the convergence of the goals and strategies of fire hazard reduction and

ecological restoration in the forests of the western United States. For example,

the official position of the Society of American Foresters in response to the

2000 fire season included the statement that.

“The buildup of combustible materials (fuels) in the forests of the

West is at an all-time high. Much of this can be attributed to the

decades of fire suppression that allowed the fuels to build up so fires

will now burn bigger and hotter than ever” — Society of American

Foresters, August 11, 2000, press release. 1Department of Geography, University of

Colorado, Boulder, CO.
260 USDA Forest Service Proceedings RMRS-P-29. 2003.

There is a widespread belief among resource managers, reflected by many

of the papers presented in the current conference, that fuel accumulation during nearly a century of fire suppression in western forests was the major cause

of the widespread wildfires of the 2000 season. Likewise, there is a consensus

that a perceived decline in “forest health” (tree diseases, mistletoe infection,

and forest insect pests) is the result of fire exclusion. One of the leading

experts on ecological restoration in western U.S. forests has written:

“The dry forest ecosystems of the American West, especially those

once dominated by open ponderosa pine forest, are in widespread

collapse. We are now witnessing sudden leaps in aberrant ecosystem

behaviour long predicted by ecologists and conservation professionals

(see Nature 407, 5; 2000). Trends over the past half-century show

that the frequency, intensity and size of wildfires will increase – by

orders of magnitude – the loss of biological diversity, property and

human lives for many generations to come.” — Covington 2000,

p. 135.

The view that current fire hazard is largely attributable to fuel buildup under decades of fire exclusion is strongly reflected in the following passage from

the National Fire Plan:

“While the policy of aggressive fire suppression appeared to be successful, it set the stage for the intense fires that we see today. ...after

many years of suppressing fires, thus disrupting normal ecological

cycles, changes in the structure and make-up of forests began to

occur. Species of trees that ordinarily would have been eliminated

from forests by periodic, low-intensity fires began to become a dominant part of the forest canopy. Over time, these trees became

susceptible to insects and disease. Standing dead and dying trees in

conjunction with other brush and downed material began to fill the

forest floor. The resulting accumulation of these materials, when

dried by extended periods of drought, created the fuels that

promote the type of wildfires that we have seen this year.

“In short, decades of aggressive fire suppression have drastically

changed the look and fire behavior of Western forests and rangelands. Forests a century ago were less dense and had larger, more

fire-resistant trees. For example, in northern Arizona, some lower

elevation stands of ponderosa pine that once held 50 trees per acre,

now contain 200 or more trees per acre. In addition, the composition of our forests have changed from more fire-resistant tree species

to non-fire resistant species such as grand fir, Douglas-fir, and subalpine fir. As a result, studies show that today’s wildfires typically

burn hotter, faster, and higher than those of the past.”— National

Fire Plan 2001.

While the National Fire Plan also recognizes the importance of other contributing factors to our current wildfire management crisis, including weather

influences (i.e., the effects of the El Niño-Southern Oscillation) and the land

use policies that have permitted uncontrolled growth at the wildland-urban

interface, this quotation represents the dominant view of the current wildfire

management problem among political leaders, resource managers, and the

general public. For convenience, I will refer to this view as the “fire exclusion/fuel buildup” perspective on current fire hazard in western U.S. forests.

An important theme of this essay is that assessment of fire hazard, and

especially ecological restoration, requires a sound understanding of historic

fire regimes in the ecosystem of interest. “Fire regime” is used here to refer to

Veblen Key Issues in Fire Regime Research for Fuels Management and Ecological Restoration
USDA Forest Service Proceedings RMRS-P-29. 2003. 261

the spatial and temporal variation of fires and their effects in a given area over

a given time period. The parameters used for describing fire regimes are briefly

discussed below under Methodological Issues. One major goal of fire regime research is to discover under what historic and present fire regimes and ecosystem conditions do the goals and methods of fuels reduction and ecological restoration converge.

The idea that current fuel levels are unnaturally high due to suppression of

formerly frequent surface fires originated to a large extent from studies of

ponderosa pine ecosystems. This viewpoint is best supported by multiple lines

of research on Southwest ponderosa pine ecosystems showing that frequentfire disturbance regimes were disrupted after Euro-American settlement

throughout the Southwest resulting in major increases in stand densities and

in larger and more intense wildfires (Moore et al. 1999). Supporting evidence

comes from numerous retrospective studies of fire-scar reconstruction of fire regimes (Swetnam and Baisan 1996, Fulé et al. 1997), tree-ring based reconstructions of past stand structures (Covington and Moore 1994, Fulé et al.

1997, Mast et al. 1999), and historical evidence from photographs and early

20th century forest inventories (Moore et al. 1999). The frequent surface fires

that had maintained open-canopy conditions declined dramatically in the late

19th century due to grass fuels reduction by introduced livestock and subsequently due to organized fire suppression activities (Swetnam and Baisan 1996,

Moore et al. 1999). Detailed studies of past fire and forest conditions support

a series of carefully planned and executed restoration projects in Southwest

ponderosa pine ecosystems (Covington et al. 1997; Moore et al. 1999). This

overall approach has become a model for a step-by-step process of conducting

careful historical ecological research followed by experimentation and monitoring of restoration treatments. Similar step-by-step approaches to ecological restoration based on area-specific research of fire regimes and past forest conditions are in earlier phases of development in ponderosa pine forests in other regions, including Colorado (e.g., City of Boulder 1999; Brown et al. 1999,

2001; Kaufmann et al. 2000, 2001; Huckaby et al. 2001; Mackes and Lynch

2003; Romme et al. 2003).

How applicable is the Southwest ponderosa pine model of fire exclusion

and subsequent changes in forest conditions (e.g., Covington et al. 1997,

Moore et al. 1999) to other forest ecosystem types and to ponderosa pine in

other regions? For example, there is abundant documentation of pre-1900

stand-replacing fires occurring in apparently denser stands of ponderosa pine

in the Colorado Rocky Mountains (Veblen and Lorenz 1986, 1991; Brown et

al. 1999; Kaufmann et al. 2000; Mast et al. 1998; Ehle 2001). The occur- rence of stand-replacing fires in some ponderosa pine forests prior to c. 1900

raises the issue of geographical variability in fire regimes for ponderosa pinedominated forests. This theme will be explored further in the next section on

ponderosa pine forests in the northern Front Range of Colorado. For other

ecosystem types, such as California shrublands and boreal and subalpine forests in Canada the validity of the fire exclusion/fuel buildup argument has

been directly challenged (Keeley and Fotheringham 2001; Johnson et al. 2001).

Thus, there is a need to conduct unique fire regime research for each particular area in order to evaluate the general applicability of the fire exclusion/fuel

buildup viewpoint.

The goal of this essay is to show the need for conducting area-specific fire

regime research to test the applicability of the fire exclusion/fuel buildup viewpoint to particular ecosystems and potential management areas. My intent is

not to evaluate the validity of the fire exclusion/fuel buildup generalization

for all the forests of the western U.S. Nor is it my intent to suggest that fuels

Key Issues in Fire Regime Research for Fuels Management and Ecological Restoration Veblen
262 USDA Forest Service Proceedings RMRS-P-29. 2003.

treatments are not appropriate for particular ecosystem types and land use

classes, or that we return to a Smokey Bear type of fire suppression policy.

Instead, I propose that the broad generalizations of the fire exclusion/fuel

buildup viewpoint be used to generate specific research questions and

hypotheses that can be critically evaluated for particular cover types and locations. This essay will identify ways in which fire regime research can support

resource planning and management decisions in both the contexts of fire hazard management and ecological restoration. I will first draw on several examples

from northern Colorado. Then I will suggest ways in which fire regime

research can better support resource planning and management decisions.

Assessing the Fire Exclusion/Fuel Buildup

Perspective in Some Northern Colorado Forests

The fire exclusion/fuel buildup perspective is based on several general premises that logically generate specific questions or hypotheses for particular

forested landscapes (table 1). Each question needs to be examined across a

range of scales from individual stands (e.g., a few hectares to 100s of hectares)

to landscape scales (e.g., 10s to 100s of square kilometers). Examples from

northern Colorado illustrate major variations in fire regimes of different forest

ecosystem types and allow comparison with similar ecosystem types in other

regions.

Table 1—Examples of some of the premises of the fire exclusion/fuel buildup viewpoint and possible areaspecific research questions.

Major premises Possible research questions to be examined for particular areas

Fire exclusion has created Do modern fire regimes differ greatly from historic fire regimes?

unnatural fuel buildup. Is there clear evidence of disruption of frequent fires that occurred before

EuroAmerican settlement?

Severe, widespread fires Did large, crown fire events occur prior to any effects of fire exclusion?

are due to unnatural

fuel buildup after

decades of fire exclusion.

Elimination of formerly What was the historic range of tree densities prior to effects of fire exclusion?

frequent surface fires has What other explanations might account for dense stands today, such as stand

created dense stands in responses to logging or abundant burning in the late 19th century or the

the modern landscape. effects of changes in grazing pressure?

Recent years of widespread, Did historic fire regimes include fire events similar to those of the 2000 fire

severe fires (e.g., the season?

2000 fire season) are due Has recent climatic variation contributed to any recent increases in fire

primarily to the effects of frequency or severity? Has climatic variation in the past resulted in fires

fire exclusion rather than of similar extent and severity to recent fires?

climatic variation.

Current levels of pathogen What was the historical variability of pathogen and insect outbreaks prior

and insect outbreaks are to fire exclusion?

unnatural and are the

consequence of fire

exclusion.

Veblen Key Issues in Fire Regime Research for Fuels Management and Ecological Restoration
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Spruce-Fir Forests: Long Fire Intervals

To evaluate the premise that fire exclusion has resulted in unnatural fuel

buildup, it is logical to ask: How different are modern fire regimes from

historic fire regimes in spruce-fir forests in northern Colorado? Spruce-fir

forests in northern Colorado have been shaped primarily by stand-replacing

(crown) fires that recur to the same point or stand at relatively long intervals,

usually much greater than a century in length (Veblen 2000, Sibold 2001,

Kulakowski 2002). Surface fires occasionally occur but to date there is no

documented occurrence of frequent (i.e., at repeated intervals of <50 years)

and widespread (i.e., affecting >8 ha) surface fires. Although crown fires in

the spruce-fir type typically kill most (>90%) of the canopy trees over large

areas (100s to over 1000s of hectares), some fires have apparently been less

intensive or less continuous resulting in younger post-fire cohorts intermixed

with older trees that survived the most recent fire (Sibold 2001, Kulakowski

2002). It is noteworthy that large percentages (i.e., >25%) of spruce-fir

forests mapped in areas of >4000 hectares and at minimum map units of c. 8

hectares do not record any stand-replacing fires in the past c. 400 years. In

other words, a large part of the spruce-fir cover type has not been significantly

affected by fire for more than 400 years.

Clearly, the fire regime of the spruce-fir cover type in northern Colorado is

characterized by infrequent, crown fires that burn large areas. High severity

fires resulting in spruce-fir stands of high tree densities are part of the natural

fire regimes of this ecosystem type (Veblen 2000). Due to the long intervals

between fires in the pre-1900 period, it is unlikely that fire exclusion has

created forest conditions that are outside the historic range of variation. Fire

history mapping in large areas (i.e., >4000 ha) at multiple sites in northern

Colorado show that the post-1900 fire regime is not unique in comparison

with time periods of similar length during the past c. 400 years (Sibold 2001,

Kulakowski 2002). Periods of 80 to well over 100 years of no widespread (i.e.,

>100 ha) fires in study areas of 4000 or more hectares are typical of the historic fire regimes of the spruce-fir cover type. Given these long intervals between

widespread fires in these spruce-fir forests, the fire-free interval that began

with fire suppression after c. 1910 is not outside the historic range of variability for this cover type.

This conclusion is specific to the c. 4000 hectare scale at which these studies were conducted and to the spruce-fir cover type. Future research at broader

spatial scales potentially may alter these research findings, but the current state

of knowledge indicates that fire occurrence in these spruce-fir forests during

the past 100 years is not outside the historic range of variability of the past c.

400 years. Thus, the premise that fire exclusion has created unnatural fuel

buildup in these spruce-fir forests is not supported. Likewise, apparent forest

health problems should not be attributed to unnaturally long fire intervals resulting from fire exclusion in the spruce-fir cover type. Indeed, widespread

outbreaks of the major lethal forest insect in this cover type, the spruce beetle (Dendroctonus rufipennis), caused massive mortality of spruce during a well

documented 19th century outbreak in northwestern Colorado long before

any significant influences of EuroAmericans on these forests (Baker and Veblen

1990, Veblen et al. 1991).

Ponderosa Pine-Dominated Montane Forests:

Spatial Variability

The long fire intervals typical of spruce-fir forests make it a relatively clear

example of where the fire exclusion/fuel buildup viewpoint is not valid, but

Key Issues in Fire Regime Research for Fuels Management and Ecological RestorationVeblen
264 USDA Forest Service Proceedings RMRS-P-29. 2003.

the situation is more complex at lower elevations in the montane zone of

ponderosa pine and Douglas-fir forests. In the northern Front Range there

are areas primarily at lower elevations and near ecotones with grasslands where

fire-scars indicate relatively frequent occurrence of non-lethal surface fires in

ponderosa pine stands prior to the early 1900s (i.e., many fire intervals

<20 years at a spatial scale of c. 100 ha; Veblen et al. 2000). Historical photographs and tree ages indicate that since the early 1900s there has been a

substantial increase in tree densities in these ponderosa pine ecosystems (Veblen

and Lorenz 1986, 1991; Mast et al. 1998). Thus, at lower elevations (and at

mid-montane sites adjacent to grasslands) there are sites in the northern Front

Range where conversion of formerly open woodlands to relatively dense stands

of ponderosa pine are qualitatively similar to the pattern widely documented

in Arizona (Moore et al. 1999). Likewise, this pattern of increased tree density under reduced fire frequency is documented for some sites in the southern

Front Range (Brown et al. 1999, Kaufmann et al. 2000). In such areas the fire

exclusion/fuel buildup viewpoint is supported.

The challenge is to determine the spatial limits of this pattern of substantial

increase in tree density following exclusion of formerly frequent surface fires. Toward higher elevation and at more mesic sites in the northern Front Range,

a variety of evidence indicates that the historic fire regime was a mixed-severity regime including both stand-replacing and surface fires. In the northern

Front Range in ponderosa pine forests, the pre-1900 frequency of fire events

inferred from fire scars declines dramatically with increasing elevation (Veblen

et al. 2000). At the spatial scale of c. 50 to 200 ha at elevations above c. 2100

m, most fire intervals are well over 50 years in length, and there is no evidence

of frequent (i.e., repeated intervals <20 years), widespread surface fires. In

these stands with relatively long fire intervals and in the surrounding areas,

the predominant age structure type is even-aged with most stands originating

between the mid-1800s and early 1900s, but with remnants of older cohorts

as well (Veblen and Lorenz 1986; Sherriff and Veblen unpublished data).

Historical photographs of the upper montane zone taken in the late 1800s

to early 1900s show that large areas of ponderosa pine-dominated forests (typically with some component of Douglas-fir and other species) had burned in

stand-replacing crown fires in the mid- to late-1800s prior to any significant

fire exclusion or unnatural fuel buildup (Veblen and Lorenz 1991). Research

is currently underway to spatially define habitats according to the relative

importance of past stand-replacing versus surface fires in shaping the current

structure of ponderosa pine-dominated stands across their full elevational range

in Boulder County (Sherriff and Veblen in progress). Our preliminary evidence indicates that except for a small area at lower elevations, on drier aspects,

and near grassland ecotones, the structure of existing ponderosa pine forests

was shaped primarily by stand-replacing fires. Over most of the surface area of

the ponderosa pine cover type in the areas where we have collected data or

done reconnaissance, the pattern of dense stands due to recovery following

19th century burning is much more common than the pattern of dense stands resulting from tree encroachment following cessation of frequent surface fires.

Although current understanding of changes of fire regimes and forest conditions in the low elevation ponderosa pine forests is consistent with restoration

of more frequent surface fires (e.g., on City of Boulder Open Space lands;

City of Boulder 1999), it does not support that prescription for the larger part

of the distribution of the ponderosa pine cover type. For much of the montane zone in Boulder County, restoration of the historic fire regime would require a significant (probably dominant) component of stand-replacing fires.

Due to the high density of residences in this area, it is unlikely that restoration

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USDA Forest Service Proceedings RMRS-P-29. 2003. 265

of the historic fire regime is feasible. Prior land-use decisions have severely

limited the current opportunities for ecological restoration. Stand thinning

and prescribed burning may be appropriate prescriptions if priority is given to reducing fire hazard, but in this case the goals of fire hazard reduction and

ecological restoration do not converge.

In addition to this lack of convergence with the goal of ecological restoration for a large part of the montane zone of the Front Range, fire hazard

mitigation through extensive thinning and subsequent prescribed fire raises

numerous contentious issues. It is uncertain how effective different intensities

of thinning will be in mitigating crown fires and protecting structures, especially under extreme weather conditions. Observations of fire behavior in areas

with and without fuels treatments are helpful, but are unlikely to be conclusive because of the other variables affecting fire spread (such as fire suppression

activities and weather). There is a consensus that thinning to create “defensible space” around structures is effective and socially desirable, but the extent

to which neighboring or remote tracts of forests lacking residences should be

thinned is disputed (Stein 2002). Likewise, there is a potential conflict between the cutting of larger diameter trees to support mitigation costs and the

desire to retain old trees for wildlife and other values. Operationally, longterm fuels management to maintain a desirable level of fuels, even if agreement

can be reached on what that desirable level is, is a formidable and presently

unresolved problem for management scientists (Hof and Omi 2002). Furthermore, economic and environmental costs of fire hazard mitigation are

extremely high as are the costs of fire suppression and of catastrophic fires.

There is much need for an informed debate over the nature of these costs

under different management scenarios and of who should pay these costs in

the context of private landowners’ decisions to locate in such hazardous

environments.

An important caveat to the above discussion is that it is based on observations and interpretations largely at the scale of individual stands of a few ha to

200 ha in extent. Although cessation of frequent surface fires does not appear

to account for currently dense stands over most of the range of this cover type

in Boulder County, it is possible that the post-1900 reduction in fire occur- rence has resulted in fewer young, post-fire stands than what would have

occurred without fire suppression. However, relatively young stands have originated after extensive logging at the end of the 19th and beginning of the 20th

centuries (Veblen and Lorenz 1991). Furthermore, widespread burning in

the late 19th century and subsequent development of even-aged post-fire stands

has undoubtedly contributed to the homogeneous age structure of the montane zone (Veblen and Lorenz 1986, Veblen et al. 2000). If fuel continuity is

contributing to increased fire hazard, the effects of logging and past increases

in fire occurrence in the late 19th century play at least as great a role as does

fire exclusion.

Fire regime research in the upper montane zone of ponderosa pine-dominated

forests in Boulder County indicates that prior to 1900, infrequent years of

exceptionally favorable fire weather are associated with evidence of extremely

widespread fire, including a major component of stand-replacing fires (Veblen

et al. 2000). That retrospective perspective, in combination with dense residential development, implies that fire hazard reduction is likely to take

precedence over ecological restoration in this area. Tree-ring evidence indicating that large areas of the montane zone burned during extreme droughts

in the past supports management that gives priority to fire hazard mitigation. Yet, at the same time the scale and severity of pre-1900 fires in this ecosystem

type raises doubts about the long-term effectiveness of fire hazard mitigation

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266 USDA Forest Service Proceedings RMRS-P-29. 2003.

activities. The complexities of developing sustainable strategies of balancing

concerns over fire hazard mitigation and ecological restoration in the montane zone of Boulder County are greater than most observers have recognized.

These complexities include the tendency to apply a single thinning prescription indiscriminately without regard to the cause of high stand densities, an

under appreciation of the importance of extreme weather in creating extreme

fire hazard, and fundamental conflicts in the values of the stakeholders in

resource management. There is an urgent need to make much greater use of

existing research results on the range of variability of these ecosystems as well

as to conduct new research to inform the debate over resource management.

Major Research Challenges for Ecological

Restoration

In the context of restoration of fire to western forest ecosystems, the first

objective is to have a sound understanding of the historic fire regime and the

potential effects of EuroAmericans on the fire regime and forest conditions.

This requires area-specific research for the ecosystem of interest. Once the

general nature of trends in the fire regime and especially the possible effects of

fire exclusion are known, there remain a number of research challenges applicable to many ecosystems targeted for restoration. The following are examples

of the most common of these research challenges.

1. What was the temporal variability of the fire regime over multi-century

reference periods?

Reference conditions should not be defined by a snapshot in time, such as

the conditions for a few years or decades at the time of extensive EuroAmerican

settlement which for most of the West is between c.1850 and 1880. Use of

reference conditions should not stress a reconstruction of static conditions at

a particular point in time. Instead, the goal should be to understand the

recent evolutionary environment of an ecosystem, which, at a minimum

requires knowledge of temporal variability over periods of several centuries.

Most importantly, the historical context should be as complete as possible to

identify temporal trends that may be related to climatic variation for one or

two centuries just prior to and during intensive EuroAmerican settlement.

For example, for the Southwest and Colorado there is abundant tree-ring

evidence showing that the second half of the 19th century was climatically

more conducive to fire occurrence than the period from c. 1790 to 1830

(Swetnam and Betancourt 1998, Grissino-Mayer and Swetnam 2000, Veblen

et al. 2000, Donnegan et al. 2001). The 1790 to 1830 period of reduced fire

occurrence in this large region has been linked to variation in the El NiñoSouthern Oscillation which has significant teleconnections to the weather of

the Southwest and the southern Rocky Mountains (Swetnam and Betancourt

1998, Kitzberger et al. 2001, Veblen and Kitzberger 2002). These decadal to

centennial scale variations in climatic influences on fire regimes during the

reference periods need to be recognized and considered in our understanding

of current and future ecosystem fluxes.

2. How was the fire regime influenced by Native Americans?

There are strong and contrasting opinions about the influence of Native

Americans on historic fire regimes in the western United States (e.g., see the

regional reviews of this theme in Vale 2002). Broad generalizations about the

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pervasive influence of Native Americans (e.g., Denevan 1992) are not testable

and lead to sterile debates unlikely to resolve the issue. Instead, the question

of Native American influence needs to be re-framed into tractable research

questions. The roles of burning by Native Americans need to be assessed for

particular ecosystem types and locations. For example, in northern Colorado

in spruce-fir forests the dependence of years of widespread fire on exceptionally dry conditions that may only occur a few times per century reduces the

likelihood that fires set by Native Americans could have had a major influence

on the structure of this landscape. In contrast, at the ecotone of ponderosa

pine forests and the Plains-grasslands, fuel dessication is sufficient in most

summers so that anthropogenic ignitions are more likely to have spread and to

have burned significant areas. Potentially, comparative studies of areas with

and without archeological evidence of human occupation can detect past

effects of Native Americans on fire frequency and seasonality through fire-scar

studies (e.g., Kaye and Swetnam 1999). However, determination of a detectable human influence on fire frequency does not directly address the larger

question of how significantly landscapes were modified by burning by Native

Americans. Multiple lines of evidence, including reconstructions of vegetation from fossil pollen and historical observations of early explorers, may

improve our understanding of this issue, but it is likely to remain highly

controversial.

3. How did native and introduced herbivores affect fuels and fire regimes?

It is widely recognized that in some ecosystems, such as in Southwest ponderosa pine ecosystems, fire occurrence declined with the introduction of sheep

and cattle, which must have reduced grass fuels (Swetnam and Baisan 1996).

However, variations in the populations of native herbivores such as bison,

deer and elk due to Native American hunting or natural causes potentially had

significant impacts on quantity and type of fuels in some ecosystem types.

Browsing and grazing by large herbivores can either increase or decrease the

success of tree establishment, depending on the tree species and competing

shrub and herbaceous species. Early predator control efforts in some areas

may have resulted in irruptions of populations of large herbivores that changed

vegetation composition and structure early in the 20th century. These potential influences of fluctuating populations of large herbivores on fine fuels and

stand structures have received relatively little research attention (but see Fulé

et al. 2002).

4. How have invasive plant species altered fire regimes?

Exotic plant species potentially can change the fire regime by changing fuel

continuity. Cheatgrass (Bromus tectorum) is an introduced grass that has increased the fire hazard over millions of acres in the western United States

(Menakis et al. 2003). Thus, restoration of fire to some ecosystem types needs

to take into account that the potential for fire spread and intensity has been

significantly altered by such fuel changes.

5. What was the spatial variability of the fire regime within a particular

ecosystem type?

As stated previously, different locations of the same forest ecosystem type

have had different historic fire regimes for a variety of reasons: subtle differences in climatic seasonality, lightning patterns, understory characterisitics,

site productivity (related to geology, soils, and/or climate), and potentially

use by Native Americans. Such factors constitute the geographic context for

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particular ecosystems of a given cover type, such as the ponderosa pine cover

type. Geographical context is likely to account for some of the differences

reported for fire parameters like frequency and severity within the ponderosa

pine cover type. Forest cover types determined solely by the physiognomic

dominant (such as ponderosa pine) are too broadly defined to expect them to

have uniform fire regimes. Forest cover types with broad geographical distributions are likely to exhibit significant differences in historic fire regimes due

primarily to regional scale climatic variation. At a local scale, spatial variability

is also important within the same cover type as previously illustrated by the

spatial variability within the ponderosa pine cover of the northern Front Range

(Veblen et al. 2000). Spatial variation at both local and broad scales needs to

be better understood to avoid over generalizations about fire regimes at the

level of forest cover types.

Methodological Issues

Most methodological discussions of fire history techniques have focused

on the description of fire regimes from the basic descriptors of fire frequency

and area burned or their analytical derivatives such as mean fire interval or fire

rotation (e.g., Arno and Peterson 1983; Johnson and Gutsell 1994; Baker

and Ehle 2001). However, ecological understanding of the effects of past fires

requires a much more comprehensive description of a fire regime including

spatial pattern, severity, effects on tree demography, and interactions with other

disturbances (table 2). For modern fires a wide variety of techniques and data

sources (e.g., maps of the pre-burn vegetation, field sampling, remote sensing

and monitoring) can be used to obtain comprehensive descriptions of the

basic descriptors of fire events and their ecological effects. In contrast, in retrospective studies of fire regimes, quantification of the descriptors is unlikely

to be completely accurate.

Table 2—Some basic descriptors of fire regimes of potential use in historic fire regime studies.

Descriptor Definition and comments

Fire frequency This is the number of fires per unit time in some designated area

(Romme 1980).

Fire area The surface area burned by each fire. Spatial variability of severity

within the burn perimeter is often difficult to determine in retrospective

studies, especially those based primarily on fire scars. Often perimeters

are assigned to fire areas even though it is known that some

undetermined amount of the area within the perimeter did not burn.

Fire spatial pattern This is a description of the spatial pattern of the area burned in relation to

the spatial heterogeneity of the abiotic (slope, aspect, elevation) and

biotic environment (species composition, stand structure, stand age).

Severity Severity is usually measured as the amount of damage done by the fire

(e.g., tree basal area killed, height of scorching) but in some situations

responses to the post-fire environment may indicate severity (e.g., tree

-growth releases, amount of post-fire tree establishment, sedimentary

records of hydrological and depositional changes).

Fire effects on This includes changes in tree establishment or mortality rates that can be

tree demography linked to the fire.

Interactions with This includes the timing and severity of other disturbance events such as

other disturbances insect outbreaks, pathogen attacks, and wind throw that may either

influence or be influenced by the fire event.

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Fire history in forested areas can be described quantitatively on the basis of

two principal types of tree-ring evidence: dates of fire scars (fire-interval approach) or age of stands that presumably regenerated following stand-replacing

fires (stand-origin approach). The fire-scar based approach usually provides

annual (or even seasonal) resolution of the dating of past fire events but is

limited in its ability to determine the spatial extent of past fires. Two important limitations of the fire-scar based method that have long been recognized

are the lack of scar evidence of some fires and the disappearance of fire-scar

evidence due to tree death and gradual decay or the consumption of firescarred trees in more recent stand-replacing fires (Arno and Sneck 1977;

McBride 1983; Agee 1993). Because not all fires leave scars, fire-scar evidence

should be regarded as an index of past fire occurrence rather than as a complete record of past fire. Absence of the evidence (the fire scar) is not necessarily

evidence of absence of the event (the fire). Fire scars as an index of past fire

occurrence may not record fires under certain circumstances (e.g., when trees

are not the appropriate species or size to record a scar or when subsequent

fires destroy the evidence of earlier fires). Furthermore, the locations of firescarred trees may not be representative of the unsampled landscape, and

subjective (targeted) selection of sample trees may be a source of bias. Opinions vary widely about the importance of these limitations, how to best sample

the landscape for fire-scarred trees, and how to compute and interpret the

quantitative information from fire-scar dates (Johnson and Gutsell 1994;

Swetnam and Baisan 1996; Lertzman et al. 1998; Baker and Ehle 2001).

In more mesic forests where crown fires are common and major episodes of

tree establishment typically follow fire, fire history studies are based on the

dating and mapping of stand origins (Johnson and Gutsell 1994). There are

numerous potential sources of error in this approach as well. These include

the difficulty of identifying the oldest trees in post-fire cohorts and of precisely determining tree germination dates (Kipfmueller and Baker 1998). The

occurrence of time lags of variable duration between the fire event and tree

establishment may be a major source of error in dating fires, particularly if fire

scars do not clearly narrow down the range of possible dates. Recognition of

post-fire cohorts is sometimes difficult when the same patch has been affected

by multiple burns that each kill only part of the tree population and create

several post-fire cohorts. In some cases it may be difficult to distinguish between post-fire cohorts and tree establishment following other disturbances

(blowdown, insect outbreaks) or the influences of climatic variation on tree

demography. One of the most intractable problems is the “overburn problem.” The stand-origin method is based on the observation that fires are

stand-replacing, which means that part or all of the evidence of previous fires

may be destroyed by more recent burns. The determination of areas burned

by previous fires is imprecise because decisions must be made about how to

draw the perimeters of earlier fires based on often extremely fragmentary evidence or subjective estimates of past fire spread. The difficulty of determining

past fire perimeters varies widely from event to event. For example, the perimeter of the most recent crown fire usually can be reliably estimated but the

former perimeter of a centuries-old burn which has been partially burned over

by several subsequent burns may be impossible to determine accurately. The

most common summary statistic used in stand-origin studies is fire rotation,

which is the time required to burn the entire study area (Romme 1980). Since

fire rotation requires accurate measurement of past fire areas, the rotation

statistic may be seriously inaccurate.

Fire regime researchers take many measures to assure that they properly

recognize the field evidence of past fires, sample them effectively, and

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interpret them appropriately. Nevertheless, in most fire regime studies there is

uncertainty about the accuracy and completeness of the fire regime reconstruction. Furthermore, there is substantial difference of opinion about the

appropriateness and utility of different summary statistics of fire regimes

(Johnson and Gutsell 1994; Swetnam and Baisan 1996; Huggard and Arsenault

1999; Minnich et al. 2000; Baker and Ehle 2001). In the current volume,

Falk and Swetnam (2003) explore the spatial scaling dependency of fire frequency distributions with the aim of developing scaling rules for high frequency

fire regimes. Baker and Ehle (2003) examine some of the uncertainties of

estimating mean fire intervals in ponderosa pine ecosystems, and conclude

that past studies have over-estimated fire occurrence in these systems. MacLean

and Cleland (2003) demonstrate the application of geostatistical procedures

to historical land survey data to better estimate the spatial extent of fires from

surveyors’ notes. Although such approaches are important to pursue, they are

unlikely to completely remove the uncertainties in reconstructions of historic

fire regimes and their effects on forest conditions.

Given the limitations of both fire-scar and stand-origin methods of describing fire regimes, and the uncertainty that either improved sampling procedures

or analytical techniques can remedy these problems, I propose some recommendations to fire regime researchers:

1. Clarify objectives and assess reliability of methods. For example, a fire-history

study based solely on fire-scar data can produce valid and useful analyses of

temporal trends of fire occurrence in relation to land use or climatic variation, but will usually not yield a comprehensive description of the fire regime.

If the goal is to assess past fire severity and effects on tree demography, then

evidence of tree age population structures, tree mortality, and/or tree growth releases are also necessary.

2. Use multiple lines of evidence to interpret past fire regimes. Evidence of past

fires should be collected from as many different sources as possible. Whenever feasible, tree-ring evidence should be complemented by written sources

(e.g., General Land Office surveys and other landscape descriptions) and

historical photographs.

3. Researchers should present their reconstructions of past fire regimes and stand

conditions as estimates. Particularly due to the problem of disappearing evidence it is unlikely that all fires will be recorded over time periods of many

centuries. This is less of a problem in fire regimes of exclusively non-lethal

surface fires, but is a major problem in mixed-severity and crown fire systems due to destruction of evidence by the more recent stand-replacing

fires. Ranges of parameters should be given based on alternative interpretations of the accuracy and precision of the data.

4. Researchers should not overemphasize summary statistics such as mean fire

intervals or fire rotation. Mean fire intervals (both composite and individual

tree intervals) have an uncertain ecological meaning. To place too much

emphasis on the statistical significance of differences in mean fire intervals

is dangerous because of the probable inaccuracy of recording all fire events.

Likewise, fire rotation is unlikely to be measured accurately because of the

difficulty of measuring the perimeters of past fires from either fire scars or

from the fragments of post-fire cohorts that may have survived more recent

fires.

5. Researchers need to report full descriptive data and ranges of estimated parameters to the resource management community. Restrictions on publication

space in peer-reviewed journals often allow presentation of only

concise summary statistics to describe fire regimes. Researchers should make

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USDA Forest Service Proceedings RMRS-P-29. 2003. 271

available to managers full descriptions of the unreduced data sets. For

example, reports to management agencies should include full fire history

charts including all sample trees rather than composite lines that summarize the data from numerous trees. Likewise, reports should include complete

stand-origin maps and detailed accounts of the procedures used for reconstructing the perimeters of past fires.

For managers concerned with ecological restoration I make the following

recommendations about using the results of fire regime studies:

1. Do not adopt summary statistics such as the mean fire interval as management goals. These are rarely accurate enough to justify mimicking them,

and in addition, changes in other variables (e.g., climate, herbivores, invasive plant species) may make them inappropriate as management

prescriptions. Instead of attempting to mimic a potentially inaccurate summary statistic of a fire regime (i.e., mean fire interval or rotation period),

managers should consider multiple lines of evidence (e.g., tree population

age structures, historical photographs, repeated twentieth century inventories or surveys) that help the manager to identify trends or trajectories in

ecosystem conditions that may be related to changes in fire regimes. Quantitative fire history data are vitally important to management decisions but

they need to be presented in their entirety (e.g., as complete charts of firescar chronologies for individual trees and as stand-origin maps) rather than

as mean fire intervals and rotation periods. The manager should be able to

see the trends and management implications in the more basic data compilations instead of relying on summary statistics that often have inherent

limitations due to incomplete preservation of the evidence of past fires.

2. Define goals in terms of ranges of desired vegetation conditions. Precise and

accurate descriptors of the vegetation conditions are unlikely to be obtained.

More broadly defined ranges of desired management conditions is consistent both with the uncertainties of reconstructing past conditions from

fragmentary evidence and with the notion that ecological heterogeneity is

often more desirable than homogeneity. Managing for a broader range of

conditions builds some buffering into the management plan to account for

surprises such as mortality events caused by insect outbreaks. In most cases,

greater heterogeneity resulting from a range of management prescriptions

is likely to contribute to management success.

3. Require area-specific data and analyses to support management decisions.

Studies conducted elsewhere rarely yield results or a model of fire regime

and past stand conditions that can be uncritically applied to an unstudied

situation. This is true even for the same forest cover type. Thus, the findings from studies done off site may at best be used as insights into the

formulation of hypotheses to be tested by data collection and analysis in

new study areas or management units.

4. Use adaptive management and monitoring to assess management success.

Current knowledge of ecosystem dynamics is incomplete and may change

in ways that are important for the goals of ecological restoration. Management goals and strategies should be regarded as hypotheses to be tested by

future research and monitoring (Christensen et al. 1996). This requires

continued communication between managers and researchers. Managers

need to be able to adapt to inevitable surprises and trends, such as unpredicted

diseases and forest pest outbreaks as well as climatic variation. Adaptability

and accountability require that a high funding priority be given to monitoring programs that compare expected outcomes with objective measures of results.

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272 USDA Forest Service Proceedings RMRS-P-29. 2003.

Conclusions

This essay was written to introduce some of the key limitations and issues in

fire regime research in the context of wildfire hazard management and ecological restoration. One of the principal messages is that broad generalizations

and premises need to be carefully examined for particular ecosystems and

management objectives. The goal of the essay has not been to challenge the

widespread consensus that fire exclusion has had undesirable consequences in

many western forest ecosystems. Rather, it is hoped that critical evaluation of

the premises of the fire exclusion/fuel buildup viewpoint for particular ecosystem types and locations will help to avoid inappropriate or ineffective

management strategies. Forest ecosystem types with demonstrated historic

fire regimes of frequent surface fires and fuel buildup during the fire exclusion

period should be targeted for ecological restoration, which may also converge

with reduction of fire hazard to property and humans. In contrast, in forest

ecosystems characterized by historic fire regimes with long intervals between

stand-replacing fires, attempts to create new fire regimes of frequent surface

fires are inconsistent with ecological restoration and likely to be futile.

As implied by the subtitle of the Conference, fuels management and ecological restoration need to be attentive to “proper place” and “appropriate

time.” Fire regime research can inform management decisions about the proper

place and time for fuels management and restoration of fire to ecosystems.

Fire regime research continues to inform management decisions in useful and

important ways, but the quantity and quality of this research needs to be improved. Clearly there is a need for greater involvement of fire regime researchers

in the early phases of project planning and continued communication between researchers and managers in the monitoring phases of restoration and

fire mitigation projects.

Acknowledgments

For discussion and comments on a draft of the manuscript I thank Karen

Eisenhart, Dominik Kulakowski, Rosemary Sherriff, Bill Baker, and Jason

Sibold. This essay was partially based on research funded by the National

Science Foundation and the Biological Resources Division of the U.S.

Geological Survey.

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USDA Forest Service Proceedings RMRS-P-29. 2003. 277

Lessons From the Fires of 2000: Post-Fire

Heterogeneity in Ponderosa Pine Forests

Natasha B. Kotliar1, Sandra L. Haire1, and Carl H. Key2

Abstract—We evaluate burn-severity patterns for six burns that occurred in the southern Rocky Mountains and the Colorado Plateau in 2000. We compare the results of

two data sources: Burned Area Rehabilitations Teams (BAER) and a spatial burnseverity model derived from satellite imagery (the Normalized Burn Ratio; NBR).

BAER maps tended to overestimate area of severe burns and underestimate area of

moderate-severity burns relative to NBR maps. Low elevation and more southern

ponderosa pine burns were predominantly understory burns, whereas burns at higher

elevations and farther north had a greater component of high-severity burns. Thus,

much, if not most, of the area covered by these burns appears to be consistent with

historic burns and contributes to healthy functioning ecosystems.

Concern that the size and severity of the 2000 fires were “beyond the

range of natural variability” in ponderosa pine (Pinus ponderosa) and

mixed conifer systems has provided justification for ecological restoration programs. However, little is known about the spatial heterogeneity resulting from

recent or pre-historic fires. Here we evaluate the effects of burn-severity patterns on landscape heterogeneity for burns that occurred in the southern Rocky

Mountains and the Colorado Plateau in 2000. We compare the results of two

data sources: Burned Area Rehabilitation Teams (BAER) and a spatial burnseverity model derived from satellite imagery.

Burn-severity maps were developed using the Normalized Burn Ratio (NBR).

NBR is derived from comparisons of pre- and post-fire Thematic Mapper imagery (30 m resolution). Band 4 (near infrared) reflects changes in vegetation

greenness and soil moisture, whereas band 7 (mid infrared) reflects soil type

and moisture levels. Band 4 tends to decrease post-fire, whereas band 7 tends

to increase post fire. NBR is based on the inverse relationship between bands

4 and 7: NBR = (band 4 – band 7)/(band 4 + 7). Delta NBR values are

derived from differences in pre- and post-fire NBR scores, which in turn can

be used as an index of burn severity (higher delta NBR indicates higher burn

severity). The NBR methodology was developed to be repeatable and quantifiable, and it offers several advantages for quantifying burn severity compared

to BAER maps, which are designed for rapid assessment and targeting of highseverity burns for rehabilitation (table 1).

We created burn severity maps using delta NBR for six burns (figure 1):

Bobcat Gulch and Hi Meadow in Colorado, Viveash and Cerro Grande in

New Mexico, and Outlet and Pumpkin in Arizona. Burns in general were

dominated by ponderosa pine (Pinus ponderosa), but Douglas-fir (Pseudotsuga

menziesii), aspen (Populus tremuloides), subalpine fir (Abies lasiocarpa), and

Engelmann spruce (Picea engelmannii) were also present at some burns. Subalpine fir and Engelmann spruce were dominant at Viveash and at higher

elevations at Outlet.

Preliminary analyses indicate that in general, BAER maps corresponded

fairly well to NBR maps at these burns, but there were several discrepancies

(table 2). At the southernmost burns (Pumpkin and Cerro Grande), which

1US Geological Survey, Fort Collins

Science Center, Fort Collins, CO.

2US Geological Survey, USGS - Glacier

Field Station Science Center, c/o

Glacier National Park, West Glacier, MT.
278 USDA Forest Service Proceedings RMRS-P-29. 2003.

were dominated by ponderosa pine, at least half of the area was classified as a

low-severity burn by NBR. At these burns, BAER maps tended to underestimate areas of moderate-severity burns and overestimate high-severity burns

compared to NBR. Outlet (large areas of aspen/spruce/fir forests) and Hi

Meadow (upper montane ponderosa pine/Douglas-fir) were still predominated by low-severity burns, but moderate- and high-severity burns comprised

a greater proportion of the burns than Pumpkin and Cerro Grande. BAER

and NBR were similar at Outlet, but BAER maps indicated greater highseverity and less low-severity burns than NBR at Hi Meadow. Bobcat Gulch

was similar to Hi Meadow, but had a fairly even distribution of area across all

burn severities. BAER maps indicated greater high-severity and less moderate-severity area burned than NBR maps. Viveash (predominantly spruce/fir)

was the most severe of all six burns and was largely a high-severity burn. The

area of moderate-severity burn at Viveash was lowest in BAER maps. Visual

comparisons of BAER vs. NBR maps indicate that small-scale patchiness is

missed by BAER maps, and may reflect the differences observed in the two

mapping techniques. The BAER mapping process may underestimate moderateseverity patches in particular, which tend to be relatively small.

Table 1—Comparison of attributes of six burn-severity maps created by Burned Area

Rehabilitation Teams (BAER) and the general Normalized Burn Ratio (NBR) method.

BAER NBR

Targets areas of high-severity burns for All burn severities addressed equally

rehabilitation

Categorical data (unburned/low, moderate, Continuous data

and high severity)

Burn-severity polygons Grid of 30 m2

 pixels

Subjectivity involved in boundary definitions Standardized methodology

and severity classification; variation among

burns in methodologies and data sources

Bobcat Gulch

3,059 ha

1694-2551 m

Hi Meadow

4,422 ha

2090-2636 m

Cerro Grande

17,351 ha

1959-3075 m

Viveash

11,017 h

2441-3538 m

Outlet

5,482 ha

1902-2712 m

Pumpkin

6,384 ha

2219-3175 m

New Mexico

Colorado

Arizona

Figure 1—Burns of 2000.

Approximate locations and area of

study sites.

Kotliar, Haire, and Key Lessons From the Fires of 2000: Post-Fire Heterogeneity in Ponderosa Pine Forests
USDA Forest Service Proceedings RMRS-P-29. 2003. 279

Pronounced overall differences were observed among the six burns, which

likely reflects variation in cover types, fuels, historic fire regimes, elevation,

and topography. Lower elevation and southern sites (e.g., Pumpkin and Cerro

Grande) had the greatest area of low-severity burns compared to higher elevation (e.g., Viveash) and northern sites (e.g., Bobcat Gulch and Hi Meadow).

Although it has repeatedly been suggested that recent burns are beyond the

range of natural variation, much, if not most, of the area covered by the burns

are consistent with our current understanding of historic fire regimes. This is

especially true of the burns occurring in more northern latitudes and higher

elevation (Brown et al. 1999; Veblen et al. 2001).

It is important to recognize that although portions of some burns (e.g.,

Cerro Grande and Pumpkin) may have burned hotter than most fires in southwestern ponderosa pine forests prior to Euro-American settlement (Allen et

al. 2002), large portions of these burns did not. Indeed, the area of these

burns in low and moderate severity classes performed the desired functions of

many prescribed fires. Furthermore, the broad spectrum of burn severity observed across the six burns is important for the integrity of ecological

communities (Kotliar et al. 2002). Thus, taken overall, these burns were not

as extreme or destructive as has been frequently suggested. Although restoration of historic forest structure to reduce the risk of wildfire may be justified in

the wildland-urban interface, the premise that current wildland fires are beyond the range of natural variability and need to be controlled may not always

be valid.

References

Allen, C.D.; Savage, M.; Falk, D.A.; Suckling, K.F.; Swetnam, T.W.; Sculke, T.; Stacey,

P.B.; Morgan, P.; Hoffman, M.; Klingel, J.T. 2002. Ecological restoration of

southwestern ponderosa pine ecosystems: a broad perspective. Ecological

Applications. 12: 1418-1433.

Brown, P.M.; Kaufmann, M.R.; Shepperd, W.D. 1999. Long-term, landscape patterns

of past fire events in a montane ponderosa pine forest of central Colorado. Landscape

Ecology. 14: 513-532.

Kotliar, N.B.; Hejl, S.; Hutto, R.L.; Saab, V.A.; Melcher, C.P.; McFadzen, M.E. 2002.

Effects of fire and post-fire salvage logging on avian communities in coniferdominated forests of the western United States. Studies in Avian Biology. 25:49-64.

Veblen, T.T.; Kitzberger, T.; Donnegan, J. 2000. Climatic and human influence on

fire regimes in ponderosa pine forests in the Colorado Front Range. Ecological

Applications. 10: 1178-1195.

Lessons From the Fires of 2000: Post-Fire Heterogeneity in Ponderosa Pine Forests Kotliar, Haire, and Key

Table 2—Area (%) of burn severity classes using BAER and NBR methodologies.

BAER burn severity NBR burn severity1

Burn Dominant cover types Low Moderate High Low Moderate High

Pumpkin Ponderosa pine 71% 2% 26% 65% 15% 20%

Cerro Grande Ponderosa pine 57% 8% 34% 60% 20% 20%

Outlet Ponderosa pine Spruce/fir 39% 34% 23% 40% 30% 25%

Hi Meadow Ponderosa pine/Douglas-fir 2% 53% 45% 45% 35% 20%

Bobcat Gulch Ponderosa pine/ Douglas-fir 30% 25% 45% 33% 33% 33%

Viveash Spruce/fir 41% 11% 48% 15% 20% 65%

1 Because NBR burn-severity values are continuous, we estimated the approximate area in each burn-severity

category to compare to BAER categorical data.
280 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 281

Mapping the Cheatgrass-Caused Departure

From Historical Natural Fire Regimes in the

Great Basin, USA

James P. Menakis1, Dianne Osborne2, and Melanie Miller3

Abstract—Cheatgrass (Bromus tectorum) is an exotic grass that has increased fire

hazard on millions of square kilometers of semi-arid rangelands in the western United

States. Cheatgrass aggressively outcompetes native vegetation after fire and significantly enhances fire size and frequency. To evaluate the effect of cheatgrass on

historical natural fire regimes, we combined cheatgrass data mapped from Advanced

Very High Resolution Radiometer images of the Great Basin with Fire Regime Condition Class (FRCC) data mapped from plant succession data incorporated with several

spatial data layers for the conterminous United States. These FRCCs depict the

degree of departure from historical fire regimes resulting in alterations of key ecosystem components such as species composition, structural stage, stand age, and canopy

closure. While the FRCC data layer adequately depicted forest communities, it insufficiently depicted grassland and shrubland communities. By adding cheatgrass, FRCC

3 (areas that have been significantly altered from their historical range) increased by

20 percent on Federal lands to almost 60,522 square kilometers for the conterminous United States.

Introduction

I

n April 1999, the General Accounting Office (GAO) published a report

recommending that the Secretary of Agriculture direct the Chief of the

Forest Service to develop a cohesive strategy for reducing accumulated vegetation and maintaining it at acceptable levels on National Forests of the Interior

West (US GAO 1999). In October 2000, the United States Department of

Agriculture (USDA) Forest Service (FS) responded with the report, “Protecting People and Sustaining Resources in Fire-adapted Ecosystems: a Cohesive

Strategy” (USDA FS 2000). This FS report establishes a framework that

restores and maintains ecosystem health in fire adaptive ecosystems by directing

the agency to:

• improve the resilience and sustainability of forests and grasslands at risk,

• conserve priority watersheds, species, and biodiversity,

• reduce wildland fire costs, losses, and damages, and

• better ensure public and firefighter safety.

To assist in the FS response to the GAO report, the Fire Modeling Institute

(FMI) at the USDA FS Fire Science Laboratory, Rocky Mountain Research

Station, Missoula, Montana, created spatial data layers to provide managers

with national-level data on current conditions of vegetation and fuels (Schmidt

and others 2002). FMI developed these spatial data layers, hereafter referred

to as layers, to address the following questions (Schmidt and others 2002):

• How do current vegetation and fuels differ from those that existed historically?

1Fire Effects Unit, Fire Science Laboratory, Rocky Mountain Research Station,

Forest Service USDA, Missoula, MT.

2National Science and Technology

Center, Bureau of Land Management

DOI, Denver, CO.

3Bureau of Land Management, located

at: Fire Science Laboratory, Rocky

Mountain Research Station, Missoula,

MT.
282 USDA Forest Service Proceedings RMRS-P-29. 2003.

• Where on the landscape do vegetation and fuels differ from historical

levels? In particular, where are high fuel accumulations?

• When considered at a coarse scale, which areas estimated to have high

fuel accumulations represent the highest priorities for treatment?

They created these layers from ecologically based methods to map vegetation changes resulting from the departure of historical natural fire regimes

(Hardy and others 2001). These layers have been subsequently used in a joint

cohesive fuels management strategy developed by the FS and those Department of Interior (DOI) agencies with wildland fire management responsibilities.

In this document, we will refer to these layers as the “Coarse Scale.”

One of the key Coarse Scale layers used in both cohesive strategies was Fire

Regime Condition Class (FRCC). FRCC depicts the degree of departure from

historical fire regimes resulting in alterations of key ecosystem components

such as species composition, structural stage, stand age, and canopy closure

(table 1) (Schmidt and others 2002). During the development of the joint

Cohesive Strategy, it appeared to rangeland managers that the Coarse Scale

layers potentially underestimated the amount of area departed from historical

fire regimes in grassland and shrubland communities. This appeared to be

especially true in the Great Basin, where the exotic species cheatgrass (Bromus

tectorum) has become widespread.

Cheatgrass is an exotic annual grass that has increased fire hazard on millions of square kilometers in the western United States (USDA FS 2002).

Cheatgrass aggressively outcompetes seedlings of native vegetation after fire,

particularly in semi-arid rangelands of the Interior West. The fuel bed created

by cheatgrass results in significantly increased fire size and frequency, compared to the native shrub/grass vegetation that was historically present on

Table 1—Fire Regime Condition Classa descriptions.

Fire Regime

Condition

Class Fire regime Example management options

FRCC 1 Fire regimes are within an historical range and the risk of Where appropriate, these areas can be

losing key ecosystem components is low. Vegetation maintained within the historical fire regime

attributes (species composition and structure) are intact by treatments such as fire use.

and functioning within an historical range.

FRCC 2 Fire regimes have been moderately altered from their Where appropriate, these areas may need

historical range. The risk of losing key ecosystem moderate levels of restoration treatments,

components is moderate. Fire frequencies have departed such as fire use and hand or mechanical

from historical frequencies by one or more return intervals treatments, to be restored to the historical

(either increased or decreased). This results in moderate fire regime.

changes to one or more of the following: fire size, intensity

and severity, and landscape patterns. Vegetation attributes

have been moderately altered from their historical range.

FRCC 3 Fire regimes have been significantly altered from their Where appropriate, these areas may need high

historical range. The risk of losing key ecosystem levels of restoration treatments, such as hand

components is high. Fire frequencies have departed from or mechanical treatments, before fire can

historical frequencies by multiple return intervals. This be used to restore the historical fire regime.

results in dramatic changes to one or more of the following:

fire size, intensity, severity, and landscape patterns.

Vegetation attributes have been significantly altered from

their historical range.

a

Fire Regime Condition Classes (FRCC) are a qualitative measure describing the degree of departure from historical fire regimes,

possibly resulting in alterations of key ecosystem components such as species composition, structural stage, stand age, canopy

closure, and fuel loadings. One or more of the following activities may have caused this departure: fire suppression, timber

harvesting, livestock grazing, introduction and establishment of exotic plant species, introduced insects or disease, or other

management activities.

Menakis, Osborne, and Miller Mapping the Cheatgrass-Caused Departure From Historical Natural Fire Regimes in the Great Basin, USA
USDA Forest Service Proceedings RMRS-P-29. 2003. 283

these sites (USDA FS 2002). These repeated fires kill the remaining native

plants (Monsen 1994). Since FRCC depicts departure from historical fire regimes, a change in vegetation from shrub/grass to one dominated by cheatgrass

would change the assignment from FRCC 1 (fire regimes are within their

historical range) to FRCC 3 (fire regimes have been significantly altered from

their historical range, and the risk of losing key ecosystem components from

fire is high). See table 1 for a full definition of FRCC.

In this paper, we evaluate whether the extent of FRCC 3 was underestimated in the cheatgrass type. We conducted the evaluation by combining a

cheatgrass layer developed after the Coarse Scale project to several of the original

Coarse Scale layers.

Methods

To map the effect of cheatgrass on historical natural fire regimes, we integrated several layers from two projects. We obtained a Cheatgrass layer classified

from 2000 satellite imagery by the National Science and Technology Center

(NSTC) at the Bureau of Land Management, Denver, Colorado, for the Great

Basin. NSTC developed this layer as part of their Cheatgrass Community

Mapping and Change Detection project. From the Coarse Scale mapping

project, we used the Fire Regime Condition Classes, Potential Natural Vegetation Groups, and Current Cover Types version 2000 layers. Since Hardy

and others (2000) and Schmidt and others (2002) explained the methods

used in developing the Coarse Scale layers, we will not describe them here.

NSTC developed the Cheatgrass layer from a study of 26 scenes of

Advanced Very High Resolution Radiometer (AVHRR) satellite images collected from March 3 to June 15, 2000. NSTC examined each image for quality,

accuracy, and cloud cover, then created a Normalized Difference Vegetation

Index (NDVI) for each scene. Next, NSTC selected two of the NDVI scenes

based on comparisons to cheatgrass phenology data. The early spring scene

(March 3, 2000) represented the period when cheatgrass greens-up, and the

late spring (April 26, 2000) scene represented the period when cheatgrass

cures (browns-out). Only areas that mapped both green-up and curing were

mapped by NSTC as cheatgrass to create the Cheatgrass layer.

In the GIS, we combined the Cheatgrass layer with the Potential Natural

Vegetation Groups and Current Cover Types version 2000 layers. When combined, spatial inconsistencies occurred because the layers came from different

sources. We resolved these inconsistencies by excluding the areas mapped in

the Cheatgrass layer that did not ecologically match classes in the Potential

Natural Vegetation Groups or Current Cover Types layers. Since cheatgrass

cannot ecologically occur in high elevations or wet grasslands, we excluded

cheatgrass areas that occurred in the following Potential Natural Vegetation

Groups classes: Spruce – Fir – Douglas-fir; Western spruce – fir; Lodgepole

pine – Subalpine; Wet Grassland; and Alpine Meadows – Barren. We also excluded areas mapped as cheatgrass in the following Current Cover Type classes:

Agriculture; Urban/Development/Agriculture; Water; and Barren. These

cover types were also not mapped in the FRCC layer.

Finally, we combined the edited version of the Cheatgrass layer with the

FRCC layer. Areas where cheatgrass occurred in FRCC layer classes FRCC 1

and FRCC 2 were assigned a new FRCC class called FRCC 3 – Cheatgrass.

Areas where cheatgrass occurred in the FRCC layer class FRCC 3 stayed FRCC 3.

Mapping the Cheatgrass-Caused Departure From Historical Natural Fire Regimes in the Great Basin, USA Menakis, Osborne, and Miller
284 USDA Forest Service Proceedings RMRS-P-29. 2003.

Results

The original Cheatgrass layer provided by NSTC mapped 127,396 square

kilometers of cheatgrass in the Great Basin. From this, 22 percent (28,067

square kilometers) of the total cheatgrass area was excluded because of inconsistencies with Potential Natural Vegetation Groups and Current Cover Types

version 2000 layers. Of this, only 4 percent (1,106 square kilometers) of the

excluded cheatgrass area was in FRCC 1, 2, and 3, and the rest was in nonvegetative areas (like agriculture, urban, water, and barren). The final Cheatgrass

layer (created for this projects) mapped 99,329 square kilometers of cheatgrass

in the Great Basin.

Of the 99,329 square kilometers in the final Cheatgrass layer, 48,247 square

kilometers (49 percent) was in the original FRCC 1 and 31,672 square kilometers (32 percent) was in the original FRCC 2. These areas, totaling 79,919

square kilometers (81 percent of the final Cheatgrass layer), were re-assigned

to FRCC 3 – Cheatgrass (table 2) from the original coarse scale analysis. Since

the rest of the area in the final Cheatgrass layer, 19,410 square kilometers

(19 percent), was already in the original FRCC 3, they were kept as FRCC 3

and not reassigned to FRCC 3 – Cheatgrass.

Across the conterminous United States, incorporating cheatgrass into the

original FRCC layer increased FRCC 3 by almost 11 percent (from 735,630

square kilometers to 815,549 square kilometers) (table 2). Figure 1 compares

the difference in FRCC 3, before and after incorporating cheatgrass into the

original FRCC layer for the western United States. Of the total reassigned

area of FRCC 3 – Cheatgrass, 76 percent (60,522 square kilometers) occurred

on federal ownership, increasing the original FRCC 3 by 20 percent (from

301,892 square kilometers to 362,414 square kilometers) (table 3).

When compared to the coarse scale fire regimes data, 53,516 square kilometers (67 percent) of the reassigned FRCC 3 – Cheatgrass was in the Historical

Natural Fire Regime class III (35 – 100+ years; Mixed Severity) (table 2). In

a historical natural fire regime with more frequent fires, 19,836 square kilometers (25 percent) of the reassigned FRCC 3 – Cheatgrass was in Historical

Natural Fire Regime class I (0 – 35; Low Severity) (table 2).

Table 2—A summary of all land ownerships for the conterminous United States of historical natural fire

regimes by fire regime condition classes with cheatgrass added. Summary does not include the following

cover types: agriculture, barren, water, and urban/development/agriculture.

Fire Regime Condition Class (FRCC)

FRCC 3

(without FRCC 3 –

FRCC 1 FRCC 2 cheatgrass) cheatgrass

Historical natural fire regime Km2 Km2 Km2 Km2 Total km2

I. 0-35 years; low severity 705,430 695,976 313,605 19,836 1,734,847

II. 0-35 years; stand replacement 778,245 537,541 41,870 2,393 1,360,049

III. 35-100+ years; mixed severity 480,779 436,558 218,545 53,516 1,189,398

IV. 35-100+ years; stand replacement 210,708 142,847 141,757 4,174 499,486

V. 200+ years; stand replacement 196,511 55,470 19,853 0 271,834

Total 2,371,673 1,868,392 735,630 79,919 5,055,614

Menakis, Osborne, and Miller Mapping the Cheatgrass-Caused Departure From Historical Natural Fire Regimes in the Great Basin, USA
USDA Forest Service Proceedings RMRS-P-29. 2003. 285 Figure 1—Comparison of Fire Regime Condition Class 3 without Cheatgrass added (left map) to Fire Regime Condition Class 3 with Cheatgrass added (right map). The boundary for the Great Basin is represented by the bold line.

Mapping the Cheatgrass-Caused Departure From Historical Natural Fire Regimes in the Great Basin, USA Menakis, Osborne, and Miller
286 USDA Forest Service Proceedings RMRS-P-29. 2003.

Discussion

By incorporating cheatgrass spatial data for the Great Basin with the Coarse

Scale FRCC layer for the nation, areas mapped as FRCC 3 increased by 11

percent. This would strongly suggest that the Coarse Scale FRCC layer

underestimated FRCC 3 for rangelands and shrublands in the Great Basin.

However, it would be difficult to extrapolate these numbers to the rest of the

Continental United States. One could not use this Cheatgrass FRCC layer as

a substitute for the Coarse Scale Version 2000 layers, because cheatgrass was

only mapped for the Great Basin. The Coarse Scale layers were derived from

national level data developed with the same methods throughout the conterminous United States (Schmidt and others 2002). This approach allows for

uniform analysis and interpretation.

Unfortunately, we do not know of any national level spatial data that adequately maps exotic grasses. This might be the result of attempting to map

these cover types at the wrong scale. Much of the departure from historical

fire regimes in rangelands and shrublands involve the encroachment of exotic

species into native communities resulting in changes in fire frequency and

severity. This encroachment can be difficult to map with coarse scale spatial

data (1 square kilometer pixels), because these species rarely dominate a pixel.

Mid or fine scale spatial data (30 square meters or less) would probably be

more appropriate.

Lastly, no accuracy assessment or field verification of the layers used in this

project was conducted. Many authors have documented the difficulty in providing an accuracy assessment for coarse scale projects of 1 square kilometer

pixel size or greater (Kloditz and others 1998; Loveland and others 1991;

Schmidt and others 2002). This is because ground truth data is difficult and

expensive to collect, and would only represent a very small portion of the

study area (Schmidt and others 2002).

Management Implications

Cheatgrass has replaced native vegetation and increased fire hazard on millions of square kilometers in the western United States (USDA FS 2002). It

Menakis, Osborne, and Miller Mapping the Cheatgrass-Caused Departure From Historical Natural Fire Regimes in the Great Basin, USA

Table 3—A summary of federal land ownerships for the conterminous United States of historical natural fire

regimes by fire regime condition classes with cheatgrass added. Summary does not include the following cover

types: agriculture, barren, water, and urban/development/agriculture.

Fire Regime Condition Class (FRCC)

FRCC 3

(without FRCC 3 –

FRCC 1 FRCC 2 cheatgrass) cheatgrass

Historical natural fire regime Km2 Km2 Km2 Km2 Total km2

I. 0-35 years; low severity 150,366 227,174 142,835 16,498 536,873

II. 0-35 years; stand replacement 96,230 126,701 2,938 1,475 227,344

III. 35-100+ years; mixed severity 290,685 198,692 85,341 39,284 614,002

IV. 35-100+ years; stand replacement 115,175 41,470 69,196 3,265 229,106

V. 200+ years; stand replacement 95,257 12,710 1,582 0 109,549

Total 747,713 606,747 301,892 60,522 1,716,874
USDA Forest Service Proceedings RMRS-P-29. 2003. 287

has been most successful in invading disturbed Wyoming big sagebrush and

salt desert shrub communities, and its density and distribution have increased

significantly in many ponderosa pine, pinyon juniper, antelope bitterbrush,

and mountain brush communities (Monsen 1994). Its competitive and flammable nature makes it difficult to restore shrub/grasslands to their natural

conditions. We have not previously had a consistent assessment of the distribution and extent of cheatgrass dominated areas in the Great Basin, the area

with the greatest acreage of vegetation change caused by this species. This

study provides the Department of Interior a much-needed perspective on the

scale of the restoration effort required to convert these lands into healthy

productive rangelands.

References

Hardy, Colin C.; Schmidt, Kirsten M.; Menakis, James P.; Sampson, Neil R. 2001.

Spatial data for national fire planning and fuel management. International Journal

of Wildland Fire. 10: 353-372.

Kloditz, Christiane; Boxtel, Angelien; Carfagna, Elisabetta; van Deursen, William.

1998. Estimating the accuracy of coarse scale classification using high scale

information. Photogrammetric Engineering and Remote Sensing. 64(2):

127-133.

Loveland, T.R.; Merchant, J.M.; Ohlen, D.O.; Brown, J.F. 1991. Development of a

landcover characteristics database for the conterminous U.S. Photogrammetric

Engineering and Remote Sensing. 57(11): 1453-1463.

Monsen, Stephen B. 1994. The competitive influences of cheatgrass (Bromus

tectorum) on site restoration. In: Monsen, Stephen; Kitchen, Stanley G., eds.

Proceedings – ecology and management of annual rangelands. Gen. Tech. Rep.

INT-GTR-313. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station: 43-50.

Schmidt, Kirsten M.; Menakis, James P.; Hardy, Colin C.; Hann, Wendall J.; Bunnell,

David L. 2002. Development of coarse-scale spatial data for wildland fire and fuel

management. Gen. Tech. Rep. RMRS-GTR-87. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Research Station. 46 p.

U.S. Department of Agriculture, Forest Service. 2000. Protecting people and sustaining

resources in fire-adapted ecosystems: a cohesive strategy. The Forest

Service management response to the General Accounting Office Report GAO/

RCED-99-65, April 13, 2000. 89 p.

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station,

Fire Sciences Laboratory (2002 April). Fire Effects Information System, [Online].

Available: http://www.fs.fed.us/database/feis/ [3/20/2002].

United States General Accounting Office. 1999. Western National Forests: A cohesive

strategy is needed to address catastrophic wildfire threats. Report to the

Subcommittee on Forests and Forest Health, Committee on Resources, House of

Representatives. GAO/RCED-99-65. 60 p.

Mapping the Cheatgrass-Caused Departure From Historical Natural Fire Regimes in the Great Basin, USA Menakis, Osborne, and Miller
288 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 289

Determining the Spatial Extent of Historical

Fires With Geostatistics in Northern Lower

Michigan

Ann L. Maclean1 and David T. Cleland2

Abstract—Interpolated General Land Office fire occurrence notes were used to

determine the spatial extent of pre-European settlement fires for 26 counties in northern

lower Michigan using ordinary kriging with probability output. Best fit of a surface

was achieved using a spherical model with a lag distance of 860 meters, an angular

tolerance of 45 degrees, and consideration of anistropy. The interpolated data were

associated with Land Type Associations to determine fire rotation and occurrence

intervals for pre-European and modern day fires. The results show that modern day

fire suppression efforts have curbed the size and frequency of fires.

Introduction

Many natural resource conservation and production issues stem from

concerns regarding the effects of natural and human-caused disturbance both at the landscape and local level. These issues are international in

scope and range from loss of species diversity to threats to human safety and

property from wildfire. The need to improve our understanding of natural

disturbance and apply that knowledge to forest resource management practices is well documented (Watt 1947; Heinselman 1963, 1973, 1981; Wright

and Heinselman 1973; Borman and Likens 1979; Canham and Loucks 1984;

Sousa 1984; Botkin 1990; Forman and Godron 1991; Christensen 1993; and

Tillman 1996). In the past, fire and wind disturbance have interacted with

biological and physical components of the ecosystem to regulate patterns in

the composition, structure, and age of forested landscapes in Michigan (Whitney

1986, 1987). Today humans are also disturbing forests through resource extraction, fire suppression, recreational use, and rural development.

Understanding the beneficial or adverse effects of disturbance, such as fire

risk, is essential to conflict resolution and ultimately sustainable forest management.

Large modern day fires in the Lake States are rare due to effective fire suppression, though they do occur with devastating results. In the late summer of

1976, a fire near Seney in Michigan’s upper peninsula burned approximately

74,000 acres. The fire, started by lightning, resulted in fire suppression and

damage costs of more than $8,000,000. The Mack Lake fire, which occurred

in northern lower Michigan in May, 1980, burned more than 20,000 acres in

6 hours. It eventually burned 24,000 acres, destroyed 44 homes and buildings, and caused one fatality. Simard and Blank (1982) reported that there

have been 5 other fires in excess of 10,000 acres since 1820 within the area

burned by the 1980 Mack Lake fire. The average return interval for these fires

is 28 years. Simard et al. (1983) noted, “Given that fires will continue to

1School of Forest Resources and Environmental Science, Michigan Technological

University, Houghton, MI.

2North Central Research Station, USDA

Forest Service, Rhinelander WI.
290 USDA Forest Service Proceedings RMRS-P-29. 2003.

occur, and that critical weather conditions will occasionally prevail, there is

every reason to believe that some future jack pine fires will escape initial attack.” There have been 215 fires in Oscoda County in the Mack Lake area

within the past 16 years. Five were larger than 100 acres with 1 fire larger

than 5,000 acres. The potential for a major conflagration exists within this

area and in many other Michigan counties due to the extensive acreages of

xeric outwash plains supporting pyrophilic jack and red pine ecosystems.

Simard et al. (1983) also stated, “Each fire is only a single observation of a

complex process, and many observations are needed before patterns are observed.” Clark (1987) noted that fire regimes are inherently difficult to assess

because “the high variance associated with any low-probability event requires

large sample sizes to determine expected values.” While there are large numbers of modern fire records available for developing predictive models of fire

ignition, modeling the potential of fires of varying size may be difficult due to

the low number of observations of larger fires due to effective fire suppression. For example, of the 65,535 fires reported by state and federal agencies in

the Lake States between 1985 and 2000, only 1,104 were larger than 100

acres and 122 larger than 1,000 acres. Thus, fires larger than 100 acres represent only 1.6 percent, or 1,104 of the 65,535 fires reported during this period.

Therefore determining fire locations and extents during the pre-suppression

era may provide information essential to estimating where large fires could

occur today if undetected, or under circumstances where several fires occur

concurrently, exceeding fire fighting capacity.

A number of approaches have been taken to estimate historical fire regimes

in terms of frequency and extent or rotation. Clements (1910), Heinselman

(1973), Arno and Sneck (1977), Simard and Blank (1982), Loope (1991),

and Brown et al. (2001) used dendrochronological methods to examine fire

scars for dating fire events at particular points. They then extrapolated the

point data to represent the area under investigation. Van Wagner (1977) introduced the use of current age-class data fitted to a negative exponential

curve to calculate fire rotations such that reconstructions of past fire events

was not needed. Clark (1988a, 1988b) used stratigraphic charcoal analysis on

petrographic thin sections to reconstruct a 750 year fire history in Itasca State

Park, Minnesota. Each of these methods has advantages and disadvantages

(Agee 1993) related to adequately assessing fire regimes at appropriate or

relevant spatial and temporal scales. Area effects on estimates of fire return

intervals or fire rotations (Arno and Petersen 1983), assumptions regarding

flammability of fuels and fire behavior across heterogeneous landscapes (Gosz

1992, Brown et al. 2001), and adequacy of approaches for understanding

long-term burn patterns (Clark 1987, 1988, 1990) are among the many challenges associated with meaningfully assessing fire regimes in space and time.

For this research, the approach to estimating fire locations and extent and

subsequent interpretations involves the use of spatial statistics, specifically

kriging, to interpolate fire observations made by General Land Office (GLO)

surveyors. The original land survey by the GLO was initiated in Michigan in

1826, providing the earliest systematically recorded information on forest conditions in the Lake States. GLO surveyors noted fire locations along section

lines and at section and quarter section corners. This provides a grid of observations along transects approximately one mile apart (Almendinger 1997).

GLO records have been used to provide information on tree species composition, diameter size distribution, and disturbance patches in the pre-European

settlement forests of the Lake States (Cottam 1949; Stearns 1949; Bourdo

1956, 1983; Cottam and Curtis 1956; Curtis 1959; Loucks 1983; Whitney

1986, 1987; Frelich 1995; and Owens 2001). Our use of GLO fire observations

Maclean and Cleland Determining the Spatial Extent of Historical Fires With Geostatistics in Northern Lower Michigan
USDA Forest Service Proceedings RMRS-P-29. 2003. 291

enables us to develop spatially explicit estimates of fire frequency and rotation

intervals over an extensive geographic area.

Methodology

GLO Data Set

Microfilmed GLO notes for the section and quarter-section corners of 26

counties in Michigan’s Northern Lower Peninsula (figure 1) were converted

to ArcInfo point coverages. The coverages were rectified and georeferenced

to a Modified Albers Conical Equal Area projection. Projection parameters on

the modified projection are as follows: false easting and false northing 0 degrees, central meridian -89.50 degrees, first standard parallel 42.33 degrees,

second standard parallel 47.66 degrees, latitude of origin 41.00 degrees,

datum NAD 27, and spheroid Clark 1866. Attribute information associated

Determining the Spatial Extent of Historical Fires With Geostatistics in Northern Lower Michigan Maclean and Cleland

Figure 1—Michigan counties

(shaded) included in fire

occurrence study.
292 USDA Forest Service Proceedings RMRS-P-29. 2003.

with the point coverages includes: corner record number; X and Y geographic

coordinates of the corner; corner number; county of location, witness tree

species, diameters, azimuths and directions; noted landscape disturbances; ecosystem classification; timber composition; other observations; surveyor’s name

and year of survey.

Noted fire occurrences were utilized to determine fire point locations. Designations of visible burn area (VB), entering burned area (EB), leaving burned

areas (LB),visible burn area and fallen timber (VU), entering burn area and

fallen timber (EU), and leaving burn area and fallen timber (LU) were included in the analysis. An addition field was created in the attribute table and

labeled fire_code. A fire_code of 1 indicates a point of noted fire occurrence,

and a value of 0 indicates no notation of fire damage.

Analysis Procedures

The ESRI ArcGIS (Version 8) Geostatistical Analyst extension was used to

perform the analysis. This extension provides advanced surface modeling using deterministic and geostatistical methods. It also bridges the gap between

geostatistics and GIS through integration of interpolation procedures into the

ArcGIS software. The software provides the capability for analyzing data sets

using different kriging approaches (simple, ordinary, probability, co-kriging,

indicator and disjunctive), evaluating variogram or covariance plots utilizing

different surface models, and calculation and evaluation of the effects of

anistrophy.

A preliminary assessment of the data showed that the fire locations were

naturally grouped into neighborhoods across the region (figure 2). This grouping can be explained in part by the fact that certain vegetation types, such as

jack pine, are more susceptible to fire. The data were subset into these neighborhoods, and each neighborhood independently interpolated. The subsetting also

facilitated the evaluation of directional autocorrelation within each neighborhood.

Ordinary kriging was used for the interpolation of the fire occurrence data

points with output in the form of a probability map. It was chosen over simple

kriging since it requires neither knowledge nor stationarity of the mean over

the entire study area. Goovaerts (1997) noted that ordinary kriging with local

search neighborhoods amounts to estimating the local mean at each location

with data specific to the neighborhood, then applying the simple kriging estimator using that estimate of the mean rather than the stationary mean. Use of

probability of occurrence not only provided predictions of the spatial extent

of the fires, but also provided a level of confidence for the prediction.

Omni-directional variograms were generated to explore the structure of

each neighborhood. Best fit of a surface for all of the neighborhoods was

achieved using a spherical model. It is recommended by Isaaks and Srivastava

(1989) that if the sample points are located on a grid, that the grid spacing is

usually a good lag spacing. The distance between the points on a perfectly

surveyed GLO grid would be 804.67 meters. However the greatest distance

was found to be 858 meters, and distance of 860 meters was chosen to provide distance tolerance.

Glacial landforms strongly influence the location of the various vegetation

types. The direction of the landform is influenced by the direction of advancement and retreat of the glaciers. Hence the data is expected to exhibit directional

autocorrelation. Direction of anistrophy was calculated for each data set and

used in the interpolation. A directional angular tolerance of 45 degrees was

specified to account for directional variation in the north-south and east-west

section lines. This information was then used to define the shape of the search

neighborhood. The search neighborhood was divided into 4 sectors with the

Maclean and Cleland Determining the Spatial Extent of Historical Fires With Geostatistics in Northern Lower Michigan
USDA Forest Service Proceedings RMRS-P-29. 2003. 293

sector axes running NW-SE and NE-SW. This reduced the directional influence of the GLO grid on the interpolation.

The probability of occurrence interpolations was converted from ArcGIS

layer (.lyr) files to ARC grid files with a 100 meter spatial resolution. This

spatial resolution provided the same scale as the original GLO point files.

Discussion

Results of Kriging

Probabilities of fire occurrence ranged from 30 to 100%. Low probabilities

tend to be found in those ecosystems supporting long-lived, fire resistant

Figure 2—Locations of GLO fire

points and neighborhood

groupings.

Determining the Spatial Extent of Historical Fires With Geostatistics in Northern Lower Michigan Maclean and Cleland
294 USDA Forest Service Proceedings RMRS-P-29. 2003.

northern hardwood and hardwood-hemlock forests including sugar maple,

basswood, and white ash, or wetland hardwoods and mixed hardwood-conifer forests including black and green ash, silver maple, elm, and cedar. As we

were interested in looking at probability of fire occurrence greater than 70%,

the continuous probability classes were reclassified into three discrete classes

with probability ranges of 70-79%, 80-89%, and 90-100%. Probabilities < 70%

were not utilized in our analysis. The division into discrete classes facilitated

acreage calculations and further analysis of the data set. However, it is important to note that forest managers could treat the output as a continuous data

set and utilize the full range of output across the entire study area.

Applications for Research, Management, and Fire Risk

The interpolated probabilities of fire occurrence thematic layers are being

used in several research and management applications in Michigan. Through

a Joint Fire Science Program funded research project, the Great Lakes Ecological Assessment (Cleland et al. 2000; is characterizing historical and modern

fire disturbance regimes of the Lake States. This research is conducting a comprehensive literature review and documenting how fire regimes have changed

since European settlement. Spatially explicit estimates of historical and modern fire frequencies and rotations are being developed for landscape ecosystems

mapped by interagency teams. Maps are being revised where necessary based

on associations of ecological factors known to influence fire regimes. The assessment of changes in fire regimes since European settlement involves the

comparison of historical fire frequencies and rotation intervals to those occurring between 1985 and 2000. We are using a hierarchical approach to assess

interactions and spatial relationships among fire-dependent and fire-sensitive

forest ecosystems and their associated disturbance regimes at three spatial scales.

Results of these analyses are being incorporated into planning and management activities on the Hiawatha, Huron-Manistee, and Ottawa National

Forests.

Use of the landscape ecosystem approach (Rowe 1980, 1984, 1992; Spies

and Barnes 1985) is premised upon the assumption that fire behavior and risk

are related to the conditions, processes, and spatial dimensions of particular

ecosystems defined by integrating important physical and biological factors

(Cleland et al. 1997). We are testing the hypothesis that historical and modern fire frequencies and rotation intervals are significantly different among

multi-scaled ecological units a posteriori. Two principal measures of fire

regimes, fire frequencies and fire rotations, provide critical information on fire

risk (Agee 1993). Fire frequency is simply the number of fires per unit time

and area. Fire rotation is the length of time necessary for an area equal to the

entire area of interest to burn (fire cycle). This definition does not imply that

the entire area will burn during a cycle; some sites may burn several times and

others not at all. Meaningful estimates of these measures require clearly specifying the size of the area of interest. Thus identifying ecologically homogenous

areas within which fire regimes can be analyzed is an essential step in the

assessment of this process. Furthermore, mapping the location of modern and

historical fires over large areas accommodates the random distribution of fires

within smaller areas, improving estimates of fire regimes within ecologically

similar spatial units.

Figure 3 displays a preliminary natural disturbance regime map based on

aggregations or subdivisions of Land Type Associations (LTAS) for northern

lower Michigan (Albert et al. 1996, Corner et al. 1999). Each polygon was

evaluated using a number of GIS data sets, including Natural Resource

Maclean and Cleland Determining the Spatial Extent of Historical Fires With Geostatistics in Northern Lower Michigan
USDA Forest Service Proceedings RMRS-P-29. 2003. 295

Conservation Service digital soil surveys, GLO notes on tree species and diameter, a 30-meter digital elevation model, hydrography, and current

vegetation. Interpretations based on associations of ecological factors known

to influence fire regimes were made, and each polygon was assigned to one of

six fire rotation categories. The definitions for each category were based on a

synthesis of the literature.

Fire rotations usually are determined by calculating the average stand age

of a forest whose age distribution fits a negative exponential or a Weibull

function (Van Wagner 1978). For this research, fire rotations were determined

by calculating the area burned for each fire rotation category, and dividing

this area by 15 to estimate area burned per annum while assuming this to be a

conservative burned area recognition window (Canham and Loucks 1984).

Table 1 displays the historical and modern fire rotations calculated for the

draft natural disturbance regime categories in northern lower Michigan. These

results are an example of the application of interpolated GLO fire points in

landscape ecosystem analyses. The following briefly describes the landscape

ecosystem fire regime based on fire rotation forest rotation (FR) classes.

Determining the Spatial Extent of Historical Fires With Geostatistics in Northern Lower Michigan Maclean and Cleland

Figure 3—Natural disturbance

regimes overlaid with interpolated

historical fire locations with a

probability of occurrence > 70%.

FR1 sites experience frequent large

catastrophic, stand-replacing fires.

FR2 experiences less frequent large

catastrophic, stand-replacing fires.

FR3 and FR3W (wetlands)

experience relatively infrequent

stand-replacing fires. FR4 and

FR4W (wetlands) experience very

infrequent stand-replacing fires.
296 USDA Forest Service Proceedings RMRS-P-29. 2003.

FR1 represents landscape ecosystems historically experiencing frequent, large

catastrophic stand-replacing fires. These ecosystems typically occur within very

dry, flat outwash plains underlain by coarse-textured sandy soils. The preEuropean settlement dominant forest types were short-lived jack pine forests

and pine barrens.

FR2 represents landscape ecosystems historically experiencing large, catastrophic stand-replacing fires at lower frequencies, hence longer fire rotations,

than the FR1 category. These ecosystems typically occur within dry outwash

plains and ice-contact landforms underlain by sandy and loamy sand soils.

The dominant pre-European forest types were white-red pine and mixed

red-white-jack pine forests.

FR3 represents landscape ecosystems historically experiencing relatively infrequent stand-replacing fires at much longer fire rotations than the FR1 or

FR2 categories. These ecosystems typically occur within dry-mesic ice-contact,

glacial lakebed, and morainal landforms underlain by loamy sand to sandy

loam soils, and commonly occur within close proximity to fire-prone ecosystems. The dominant pre-European forest type was long-lived mixed

hemlock-white pine forests with minor elements of northern hardwood forests. Frequent ground-fires prevented succession to fire-sensitive hardwoods.

FR3W represents landscape ecosystems historically experiencing relatively

infrequent stand-replacing fires. These ecosystems typically occur within wetlands embedded within or adjacent to fire-prone landscapes. The dominant

pre-European forest types were wetland conifers including spruce, fir, and

tamarack. Fire regimes and fuel formation were likely caused by interactions

of insect and disease and large-scale blow-downs, as well as periods of drought.

FR4 represents landscape ecosystems historically experiencing very infrequent stand-replacing or community maintenance (ground) fires. These

ecosystems typically occur within mesic (moist) moraines and glacial lakebeds

underlain by fine-textured sandy loam to heavy clay and silt loams soils. The

dominant pre-European forest types were long-lived, fire-sensitive northern

Maclean and Cleland Determining the Spatial Extent of Historical Fires With Geostatistics in Northern Lower Michigan

Table 1—Historic and modern fire rotations in Northern Lower Michigan.

Historical (1800s) fires P>70%

 Fire Acres % Fire

LTA grouping regime Unit size burned burn/yr rotation

Xeric LTAs dominated by jack pine and barrens FR1 836,192 211,075 1.683 60

Less xeric LTAs dominated by white-red pine FR2 1,029,138 144,850 0.938 107

Dry-mesic LTAs dominated by hemlock-white pine FR3 1,652,410 52,396 0.211 473

Wetland LTAs adjacent to fire-prone LTAs FR3W 494,638 61,618 0.830 120

Mesic LTAs dominated by northern hardwoods FR4 3,771,745 40,862 0.072 1,385

Wetland LTAs adjacent to mesic hardwood LTAs FR4W 958,232 21,012 0.146 684

Average fire rotation- 247 years Total 8,742,355 531,813 0.406

Modern (1985-2000) fires

Fire Acres % Fire

LTA grouping regime Unit size burned burn/yr rotation

Xeric LTAs dominated by jack pine and barrens FR1 902,052 15,552 0.115 870

Less xeric LTAs dominated by white-red pine FR2 1,066,009 13,766 0.086 1,162

Dry-mesic LTAs dominated by hemlock-white pine FR3 2,052,353 7,219 0.023 4,264

Wetland LTAs adjacent to fire-prone LTAs FR3W 845,278 1,763 0.014 7,192

Mesic LTAs dominated by northern hardwoods FR4 4,340,305 3,402 0.005 19,137

Wetland LTAs adjacent to mesic hardwood LTAs FR4W 1,325,801 2,103 0.011 9,456

Total 10,531,798 43,805 0.042

Average fire rotation- 2,381 years
USDA Forest Service Proceedings RMRS-P-29. 2003. 297

hardwood and hardwood-hemlock forests including sugar maple, basswood,

and white ash.

FR4W represents landscape ecosystems historically experiencing very infrequent stand-replacing or community maintenance (ground) fires. These

ecosystems typically occur within wetlands embedded within or adjacent to

fire-sensitive, hence fire protected landscape ecosystems (FR4). The dominant pre-European forest types were wetland hardwoods and mixed

hardwood-conifer forests including black and green ash, silver maple, elm,

and cedar.

Results of this research are also being applied by the North Central

Research Station and cooperating universities as part of a fire risk assessment

of the Lake States. This effort is assessing both historical and modern fire

frequencies and rotation intervals, current vegetative conditions, and human

population densities. We are investigating historical fire regimes in addition

to modern regimes because preliminary analyses of a 1985-2000 modern fire

database suggest that areas with the potential for large fires may not be adequately identified through regression due to the low number of large fires

and the overwhelming influence of humans on fire ignition and spread. For

example, Cardille and Ventura (2001) reported more than 97% of all fires

occurring in the Lake States are due to human ignition, and 58% of all fires

larger than 100 acres are due to arson. All fires reported were suppressed by

fire fighting crews. These anthropogenic influences may obscure the elucidation of ecological factors associated with fires of different sizes. We believe

the use of data on both historical and modern fire regimes will improve estimates of where the risk of large fires is greatest. Results will also provide insight

into the effectiveness of fire suppression activities by allowing comparisons of

pre-suppression fire regimes to those occurring today.

In summary, integrating information on historical disturbance regimes with

other information such as ecological units, potential natural vegetation, and

current vegetation will aid in understanding natural disturbance regimes and

fire risk. This knowledge will also be useful in addressing a larger goal of

improving our understanding of the characteristic rate of change, technically

termed the dynamics of homeorhetic stability (Reice 1994, O’Neill et al. 1986),

that formerly distinguished and maintained landscape and local ecosystems of

the Lake States.

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Scaling Rules and Probability Models for Surface

Fire Regimes in Ponderosa Pine Forests

Donald A. Falk1 and Thomas W. Swetnam1

Abstract—Statistical descriptors of the fire regime in ponderosa pine forests of the

Jemez Mountains, New Mexico, are spatially scale-dependent. Thus, quantification

of fire regimes must be undertaken in a spatially explicit framework. We apply a

variety of analytical tests adapted from species-area relationships to demonstrate an

analytical framework for understanding scaling of disturbance regimes. A new spatiotemporal scaling index, the slope of the event-area function, can provide a useful

measure of the synchrony of events within watersheds (where fire spread regulates

the distribution of events) as well as among mountain ranges. We propose two alternative mathematical models of fire interval distributions based on inherent properties

of the fire record and the ecology of frequent-fire disturbance regimes; a discrete

probability model, and a probabilistic application of the lognormal distribution. Because they involve distribution of energy and matter, these spatial and temporal

scaling rules indicate more general disturbance event-area relationships that can

facilitate the analysis of disturbance regimes in a broader ecological framework.

Introduction

Many ecological processes scale in time and space in ways that are

determined by underlying mechanistic or stochastic processes. For

example, at the level of the individual organism, body size, growth and metabolic rates, and a variety of life history traits are related systematically and can

be expressed as allometric or bioenergetic scaling rules (Wiens 1989; West,

Brown, and Enquist 1997; Enquist et al. 1999). Recent work (Enquist and

Niklas 2001; Niklas and Enquist 2001) has shown that these scaling proprieties can be extended to the structure, composition, mass, and productivity of

complex ecological communities. The unifying force across these levels of biological organization is the efficiency of energy flow, which is a strong selective

force in organismal evolution. Ostensibly emergent properties of communities and ecosystems can thus be related to fundamental biophysical processes.

Because they involve distribution and flows of energy and materials, disturbance processes similarly can be expected to follow scaling rules in space and

time (Holling 1992; West, Brown, and Enquist 1997; Enquist, Brown, and

West 1998; Ritchie and Olff 1999). In general, we may predict that ecosystem

process will scale both spatially and temporally with the factors that regulate

such events, and not simply as a function of geometry. Before we can assess

scaling patterns in disturbances, however, we must be able to characterize

disturbance events quantitatively and measurably.

Disturbances of a particular type are often grouped together under a “regime” (Pickett and White 1985; Agee 1993). While “regimes” are often

discriminated qualitatively (e.g., “stand-replacing” vs. “surface fire” regimes),

they are usefully defined by a set of quantitative descriptors (table 1). Because

1Laboratory of Tree Ring Research,

University of Arizona, Tucson, AZ.
302 USDA Forest Service Proceedings RMRS-P-29. 2003.

a disturbance regime involves multiple dimensions, disturbance regimes comprise multivariate space, not a single dimension (Agee 1993). By using multivariate

space, we open the way for quantitative analyses, rather than verbal descriptors, to

characterize the regime at a given place and time. Moreover, by describing the

regime with a set of quantitative variables, each with associated measures and

statistical characterization, we can evaluate the natural range of variability across

locations and spatial scales and across time for any given spatial extent.

To illustrate these principles, we examine a 450-yr record of fire events in

an old-growth New Mexico (USA) ponderosa pine forest, using tools of dendrochronology. Because fires in this ecosystem generally have low to moderate

intensity of the flaming front with variable duration at the tree scale (Weaver

1951; Kilgore and Taylor 1979; Agee 1993; Allen 2001), mortality is sizedependent: most mature trees survive most fires, while most smaller and

younger trees do not (i.e., mortality decreases with size). For fires above a

threshold of temperature and exposure time, the cambium of mature trees

will be killed locally at the locus of highest exposure (Keane et al. 2001). This

dead cambium, and the tree growth response around it, creates a lesion that

persists in the wood long after the event has passed. In tree species with growth

rings that can be dated with annual or sub-annual precision, the year (and, in

many cases, season) of individual fire events can be determined exactly (Arno

and Sneck 1977; Kilgore and Taylor 1979; Dieterich 1980a; Romme 1980;

McBride 1983; Swetnam and Dieterich 1985; Veblen et al. 1999). The spatial

distribution of fire events in any given year can be assessed if samples are

georeferenced and suitably distributed across the landscape (Niklasson and

Granström 2000; Heyerdahl, Brubaker, and Agee 2002). With these tools a

record of disturbance events is created, and their distribution in space and

time becomes available for analysis.

Disturbance regimes are ultimately composed of events. Where these events

are discrete, they can be mapped in space and time, and their distribution and

scaling properties analyzed. In this paper we apply an analytical framework

adapted from species-area relations and macroecology to study scaling effects

in the disturbance regime. In effect, we substitute fire dates for species and

then evaluate spatial and temporal scaling properties of the disturbance

Table 1—Dimensions of fire regimes. Adapted and expanded from Agee (1993), Johnson (1992), Whelan (1995),

White and Pickett (1985).

Dimension Typical units or metrics

Temporal distribution:

 Frequency (f ) Number of events time-1 .

 Interval (i ) Number of yrs between events; yr event-1 (= 1/f ).

 Interval variability Statistics of central tendency (mean, median, mode) and higher moments of the

(temporal frequency distribution (variance, kurtosis, skewness).

heterogeneity)

 Duration Elapsed time of a single event over a defined area (from a point to entire extent).

 Fire cycle In a stand-replacing regime, mean age of a stand with a modeled age distribution.

 Fire rotation Mean number of years required for fires to burn a specified amount of area.

 Seasonality Intra-annual occurrence for a single event or group of events.

Spatial distribution:

 Extent Total area covered (km2

).

 Spatial heterogeneity Proportion of burn area by intensity or severity class (%); patch size (ha) and

aggregation, fractal dimension (D).

Intensity Physical properties, e.g.: flame length (m), fireline intensity (kW m-1 ), rate of spread

(m hr-1), energy output per unit time (BTU hr-1 m-2 or kJ m2

), peak temperature ( ̊C.),

residence time (min).

Severity Effects on biotic and abiotic elements of the community, e.g.: mortality by species (%).

Falk and Swetnam Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests
USDA Forest Service Proceedings RMRS-P-29. 2003. 303

regime. We ask: Do quantitative descriptors of the fire regime scale in space

and time? Is there an underlying probability distribution that can describe

intervals between fire events? And finally, do these scaling relationships reflect

governing biophysical processes, such as entrainment of the fire regime by

climate?

Methods

Study Site

Monument Canyon Research Natural Area (MCN) is located in the western Jemez Mountains of north-central New Mexico, USA (figure 1). MCN

was among the first Research Natural Areas in the United States, and includes

stands of Pinus ponderosa var. scopulorum (ponderosa pine) and other conifers

more than 420 years old (Touchan and Swetnam 1995; Touchan, Allen, and

Swetnam 1996). At elevations of 2,438-2,560 m (8,000-8,400 ft), MCN is

near the upper elevation limit for ponderosa pine dominance (Regional forest

type 122.3, Petran Montane Conifer Forest) on mixed topography in northern New Mexico (Brown and Lowe 1980). As elevation increases above this

level, forest communities transition to mixed-conifer types (121.3, Petran

Subalpine Conifer Forest) regardless of aspect. In MCN, mixed conifer stands

are found mostly on protected north-facing slopes and some small drainage

bottoms. Soils are derived largely from tuff that formed the caldera of a large

(10 km radius) Pleistocene volcano. The study area encompasses the entire

256 ha (1 mi2

) RNA, of which approximately 80% is relatively level mesa-top

and 20% steep north-northeast slopes dominated by shade-tolerant conifers,

including Pseudotsuga menziesii var. glauca (Rocky Mountain Douglas-fir),

Abies concolor (white fir), and Pinus flexilis (limber pine).

Field Methods

We established a sampling grid with 200 m spacing across the study area,

using GPS field-accurate to +7 m (figure 2). Each point was permanently

marked with metal rebar and tagged for future relocation. At alternate points

(400 m lateral spacing; N = 40), we located and sampled an average of four

fire-scarred trees (min = 2, max = 12) showing evidence of the largest number

of fire scars within a 40 m radius (approximately 0.5 ha) (Niklasson and

Granström 2000; Heyerdahl, Brubaker, and Agee 2001). Further details of

field sampling protocols are provided elsewhere (Falk 1999).

Specimen Preparation and Data Reduction

Fire-scar specimens (partial or full cross-sections) collected in the field were

prepared and analyzed at the University of Arizona Laboratory of Tree-Ring

Research (LTRR), using standard techniques in dendrochronology (Dieterich

and Swetnam 1984; Fritts and Swetnam 1989). Each section was crossdated

to a local master chronology, to ensure accuracy of dating trees with locally

absent or missing rings. Once the entire ring sequence for each specimen was

dated, we recorded the year of each visible fire lesion.

Data Analysis

Fire dates for each tree were entered into FHX2, a software program designed specifically for fire history analysis (Grissino-Mayer 2001). The resulting

Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests Falk and Swetnam
304 USDA Forest Service Proceedings RMRS-P-29. 2003.

data set is a years ¥ trees matrix, in which each cell is valued as 1 (fire recorded

for that year) or 0 (no fire recorded). Because individual trees do not always

record every fire event in their vicinity, we made a composite record for each

grid point consisting of all fire dates recorded by any tree at that location

(Dieterich 1980b). Thus, the grid-based 0.5 ha search area constituted our

minimum resolution or Minimum Map Area (MMA) for reliable reconstruction of the fire record. For the same reason, fire occurrence is recorded only as

presence (1) or absence (0), not relative abundance (e.g., proportion of trees

recording a fire).

Figure 1—Site map of Jemez Mountains and Monument Canyon study area. Figure courtesy

of K.L. Beeley (NPS) and C.D. Allen (USGS).

Falk and Swetnam Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests
USDA Forest Service Proceedings RMRS-P-29. 2003. 305

Fire history metrics. For each grid-point composite, we calculated statistical measures of the fire regime (table 1), including total number of events

detected, measures of central tendency (arithmetic mean, median, and mode)

for fire intervals (yr event-1), and higher moments (skewness and kurtosis).

Analytical tests. We applied a series of procedures commonly used in

species-area relations to test for spatial and temporal scaling relationships in

the statistical descriptors of the fire regime. We did so by substituting “fire

years” for “species” in the 450-yr record of events. In a surface fire regime

such as the one studied here, subsequent fires often do not eliminate the evidence of prior events. Thus, a given site can retain a record of individual fire

events hundreds of years long, and individual trees with >20 scars are not

uncommon. This contrasts with high-intensity, stand-replacing regimes where

large, catastrophic fires destroy the tree record of prior fires on a given location (Heinselman 1981; Romme 1982). The retention of multiple fire records

at a single point in space allows mapping the spatial extent of individual fires,

given that not all trees near the perimeter of a fire may record, and that

Figure 2—Sampling grid at Monument Canyon RNA.

Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests Falk and Swetnam
306 USDA Forest Service Proceedings RMRS-P-29. 2003.

multiple independent fires within a single year could not be discriminated.

Tests used included:

Accumulation functions (“collector’s curves”) for fire events. We tested

for the effect of sample size (number of trees) on the number of fire events

detected and mean fire interval, using a bootstrap resampling program (SSIZ)

developed at the LTRR (Holmes 1995). SSIZ complies a list of fire dates and

calculates the mean fire interval with confidence limits for randomly selected

subsets of the original data set from 1... N trees. We ran 1,000 iterations of

the resampling procedure without replacement.

Event-area relationships. The relationship between species richness and

area of a sample or ecosystem is among the most widely studied patterns in

species biogeography (Arrhenius 1921; MacArthur and Wilson 1967; Connor

and McCoy 1979; Palmer and White 1994; Rosenzweig 1995). At small scales

the accumulation (“collector’s”) curve dominates (Pielou 1977; Magurran

1988), but once sample size is sufficiently large, the number of species encountered increases as a power function of area, s = cAz

, where s is species

richness, c a scaling constant that varies among organism groups, A is sample

area, and z a rate constant that varies with ecosystem type and biogeographic

scale (MacArthur and Wilson 1967; Rosenzweig 1995). With logarithmic transformation, the relationship is a linear function, log s = log c + z log A. The

value of s at the y-intercept (c or log c) reflects “point” (alpha) diversity. Steeper

values of the slope term z indicate faster accumulation of new species with

increasing area. Because z < 1 (most published values are in the range

0.15 - .35), the rule says in effect that small areas are more species-rich per

unit area than large areas.

In the current context, we can assess event-area relationships in two ways.

First, we can count the number of fires (f ) detected in a sample; this will stand

as equivalent to species richness (S). Because fires are events in both space and

time, we must define the sample temporally as well as spatially; this is conventionally done by calculating fire frequency, which is the number of fires per

unit time, giving f units of fires time-1. A related measure, fire interval, is the

number of years between fire events, or 1/f, with units of yr fire-1. Both frequency and interval can be expressed statistically as mean, median, or modal

values, and their higher moments calculated.

We tested for the effect of area by making spatially explicit subsets of the

data set. Because the data are from known locations, we can create composite

fire chronologies for any defined area within the study site. This can be accomplished by creating either nested series of samples beginning at any point,

or non-nested samples of varying area centered on random points in the study

site up to the full extent of the study (figure 3) (Palmer and White 1994).

Although not reported here, sample size and sample area can be varied independently in our study design (Falk 2003 in prep.).

The interval-area relationship. The time interval between fires is of direct

ecological interest because it represents the time between fire events. This

interval is potentially important in forest demography because of size-dependent mortality: seedlings and saplings are unlikely to survive fires (Ryan and

Reinhardt 1988; Peterson et al. 1994), so if the time interval between events

is short, the probability increases that they will be exposed to a lethal event.

By contrast, longer fire-free intervals allow young trees to attain size and morphology that makes their survival more likely (although fires occurring after

very long intervals may be more intense, due to accumulation in the larger

fuel sizes and changes in vertical fuel structure).

Falk and Swetnam Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests
USDA Forest Service Proceedings RMRS-P-29. 2003. 307

Figure 3—Nested and non-nested methods for creating simulated search areas for calculation

of composite fire statistics. Fire dates are composited within each box to test for sample area

effects.

Both f (fire frequency) and MFI (mean fire interval = 1/ 

f ) can be tested

for area-dependence in a fashion similar to species richness. The predicted

event-area function for frequency is a power law,

f = eAy

, hence log f = log e + y log A, y > 0

where e and y are scaling constants analogous to c and z in the species-area

relationship. Similarly, the predicted interval-area relationship for mean fire

interval over any defined period t is

MFIt = pA-y, and log MFIt = log p - y log A, p = e-1 .

We expect f (A) to have a positive slope (i.e., y > 0), since sampling larger

areas across the landscape should encounter more fires in a patchy fire regime

(Arno and Peterson 1983). Likewise, MFIt (A) should be negatively sloped,

because if larger area samples detect more fires, the denominator of the interScaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests Falk and Swetnam
308 USDA Forest Service Proceedings RMRS-P-29. 2003.

val statistic increases. The decreasing fire interval statistic can also be interpreted as follows: as larger areas are sampled, the likelihood increases of a fire

occurring somewhere in any given year.

Probability models for interval distributions. Fire interval probabilities

have most commonly been modeled using 2- and 3-parameter versions of the

Weibull distribution (Johnson and VanWagner 1985; Johnson 1992; Johnson

and Gutsell 1994). Although alternative models have been inadequately explored, use of the Weibull to model fire interval probabilities has become

widespread (Clark 1989; Johnson 1992; Agee 1993; Swetnam and Baisan

1996; Gardner, Romme, and Turner 1999; Grissino-Mayer 1999).

The Weibull distribution is a continuous probability model that describes

the effects of stress accumulation, and hence is often used to model time to

failure (e.g., metal fatigue, breaking points of materials, etc.) (Bain and

Engelhart 1987; Johnson, Kotz, and Balakrishnan 1994). However, it is not

clear that continuity is an appropriate assumption for fire regimes in the Southwest. Fire data in the dendroecological record are inherently annual in their

resolution, as reflected in the record of annual fire dates derived from analysis

of fire scars or establishment dates. One way of making this evident is by

attempting to increase the resolution of the record, which should be possible

in a truly continuous distribution. When we decrease the time interval to subannual patterns of fire occurrence, we are asking a different question (i.e.,

seasonality), not simply improving the precision (i.e., smaller units) of a fire

interval estimate. Thus, the fire scar record is not infinitely divisible as is typically assumed for continuous data.

A related implication of a continuous distribution is that observations are

unitary. For example, we do not interpret a temperature of 32 ̊ as the sum of

two temperatures of 16 ̊, nor a blood pressure of 120 mg as the sum of 80 mg

and 40 mg. In fire history studies, the parallel assumption is that fire intervals

of t years are a single event, which permits the use of a continuous frequency

distribution.

Forest fires in ponderosa pine ecosystems may not conform to this unitary

assumption. Both the fire record and fire events are composed inherently of a

series of discrete, binary events. In southwestern forests, fire occurrence has a

finite probability each year, with an outcome of fire or no fire. Thus, each year

can be defined as a Bernoulli trial, where the outcome is one of two possible

states (0,1) (Bain and Engelhart 1987). A fire interval of 10 yr is thus the

accumulation of 10 separate no-fire years. In this respect, discrete probability

models such as the negative binomial (years before the first success) may be

more appropriate null model. An assumption of Bernoulli probability is that

each trial is independent, whereas fire (or its absence) in year t-1 may have

some effect on the probability of fire in year t.

Here we propose an alternative approach based on first principles in fire

ecology and mathematical statistics. The capture of a fire scar in a sample is

the result of a series of contingent events, each with its respective probability

distribution (sufficient fuel, proper fuel moisture and wind, ignition source,

tree species, age and size, prior scarring survival of the fire, capture in a sample).

Thus, the eventual probability Ptot that a fire event will occur, be recorded by

the tree, and sampled by a researcher is the contingent product of the probabilities of n constituent factors:

Ptot = p1 ¥ p2 ¥ p3 ¥ p4 ¥...pn

 = i l

n

=

’ pi

 .

Falk and Swetnam Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests
USDA Forest Service Proceedings RMRS-P-29. 2003. 309

Taking the log of both sides gives:

log Ptot = p1

 + p2 + p3 + p4 +...pn = i l

n

=

Â pi

 .

Sums of random variables approach normality under the central limit theorem, provided there are a sufficient number of factors (Montroll and Shlesinger

1982). The resulting distribution of log transformed variates is thus expected

to approach normality. We therefore propose that fire intervals are lognormally distributed.

Results

Collections

For this analysis we used records from a preliminary sample of 53 firescarred trees at 16 grid points in the study area (figure 2). Most trees in the

sample were Pinus ponderosa; other species included Pseudotsuga menziesii

(Douglas-fir), Abies concolor (white fir), and Pinus flexilis (limber pine). We

restricted our analyses to the period 1600-2000, for which there is sufficient

sample size at all grid points.

Analytical Tests

Sample size accumulation function. The total number of events increased

asymptotically with sample size (figure 4-a). The function is a classic “collector’s

curve,” reflecting the capture of more fire events as sample size increases.

Notably, very small samples appear unlikely to capture the full set of fire dates

(although they probably record the most widespread events). The collector’s

curve is a saturating function, with a positive first and negative second derivative, and an asymptotic frequency of 9-10 events per century. The collector’s

curve in figure 4-a is for the full set of 53 trees across the sampled area. In a

nested multi-scale analysis, each scale would have its own collector’s curve

reaching a characteristic asymptote (Palmer & White, 1994).

Event-area and interval-area relationships. Fire dates also accumulated

in simulated samples of increasing area (figure 4-b). The lack of downward

concavity suggests high patchiness in the fire regime: with increasing area,

new events continue to be encountered at a high rate, although many of these

events were small. This area effect appears to be independent of the accumulation of fire dates with increasing sample size (figure 4-a).

Mean fire interval was strongly scale-dependent (figure 5). Following the

predicted power rule, the function is linear in log-linear space MFI a 71.1

A-0.20, r2 = 0.68. Individual plots (points in figure 5 along the y-axis) recorded

fires at mean intervals ranging from 7-23 yr (mean = 12). Extrapolation to the

tree scale (0.01-0.05 ha) suggests common intervals of 9-35 yr, although we

consider this scale below the minimum reliable spatial resolution for field verification. As data from adjacent grid points were added together to form larger

spatial composites, more fires were encountered and MFI decreased to 7 yr

for 10-ha composite samples and 4 yr for sample areas of 100 ha.

Probability model. Lognormal functions provided as close a fit to the observed distribution of fire intervals as did the Weibull distribution for spatial

composites of 1-16 grid points (figure 6). This suggests that the more

Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests Falk and Swetnam
310 USDA Forest Service Proceedings RMRS-P-29. 2003.

parsimonious and theoretically grounded lognormal distribution has potential application for modeling surface fire regimes. Interestingly, a comparison

of the computed Weibull median probability interval (WMPI) with the simple

arithmetic mean was highly correlated (i.e., little added information in the

more complex model) (figure 7).

Conclusions, Discussion, and Future Research

Directions

Scale dependence is demonstrated in the fire regime of an old-growth ponderosa pine forest. All measures of the fire regime tested appear sensitive to

sample area; number of fires and mean interval were also found to be sensitive

to sample size (number of trees). Thus, the notion of a unitary fire regime

independent of scale is untenable; instead, we see that the fire regime is a

scale-dependent characterization of an ecosystem.

Figure 4—Accumulation functions (“collector’s curves”) for the number of fire events recorded

as a function of (a) number of trees sampled and (b) sample area.

Falk and Swetnam Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests
USDA Forest Service Proceedings RMRS-P-29. 2003. 311

y = 71.063x -0.1981

R 2

 = 0.6812

1.00

10.00

100.00

10,000 100,000 1,000,000 10,000,000

Area sampled (m^2)

0 10 20 30 40 50 60 0 10 20 30 40 50 60

0 10 20 30 40 50 60 0 10 20 30 40

1 2

4 16

Figure 5—Dependence of mean fire

interval on number of trees sampled.

MFI (ordinate) decreases with

increasing area sampled (abscissa,

log scale). Data points are MFI for

simulated sample areas of differing

sizes.

Figure 6—Scale dependence of the frequency distribution of fire intervals for nested spatial samples of

1-16 grid points, with fitted lognormal (green line) and Weibull (red line) distributions.

This preliminary analysis used approximately 20% of the eventual full data

set of a275 trees at MCN. In addition to adding grid points (sampling locations), the full data set includes some sample points at closer intervals (200 m

compared to 400 m in the present analysis), providing finer spatial resolution

in the analysis. The present data set also covers approximately 150 ha; the full

MCN data set will cover approximately 250 ha. Inclusion of samples from

previous research efforts in adjacent areas of the Jemez Mountains (Morino,

Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests Falk and Swetnam
312 USDA Forest Service Proceedings RMRS-P-29. 2003.

Baisan, and Swetnam 1998; Allen 2002) will allow the tests used here to be

applied across five orders of magnitude in spatial scale (5 ¥ 10-1 - 104 ha).

Several other recent fire history studies have used a spatial array of sampling

points (Fulé, Covington, and Moore 1997; Brown, Kaufmann, and Shepperd

1999; Niklasson and Granström 2000; Skinner 2000; Veblen, Kitzberger, and

Donnegan 2000; Heyerdahl, Brubaker, and Agee 2001). A meta-analysis of

these studies may reveal even more general scaling rules.

Sample size and sample area are partly confounded in the results presented

here. Distinguishing these two factors is important, for the same reasons as in

species biogeography: the collector’s curve can dominate the species- (event-)

area function at small spatial scales (Palmer and White 1994). In the MCN

case, the grid-based design allows sample size and sample area to be decomposed using a factorial procedure, assembling a composite data set for increasing

sample size (say, 5-30 trees) selected at random from a series of increasing

sample areas (5-250 ha). The resulting data can be tested by MANOVA, or

used to generate Fisher’s a statistic for increasing species (fire date) richness

with increasing sample size. Stability in Fisher’s a indicates that increased richness is attributable only to sampling more individuals, while an increasing

Fisher’s a indicates an increase in richness independent of sample size.

A filtering approach can also be used with fire history data to identify widespread fire years. For example, Swetnam & Baisan (1996) analyzed fire data

from southwestern North America, selecting fire dates found respectively on

>25%, >10%, or any recording tree within each site (the same approach can be

applied at the site level, filtering out fire years recorded by only a few sites).

Filtering removes fire dates found on only a few trees (or sites), and is thus

useful for identifying widespread fire years. One difference with the present

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25

MFI

Figure 7—Correlation of mean and Weibull median probability interval (WMPI) for trees and

composites at Monument Canyon (N = 53). WMPI = 1.37 + 0.71 MFI (r

2

 adj. = 0.90).

Falk and Swetnam Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests
USDA Forest Service Proceedings RMRS-P-29. 2003. 313

approach is that filtering is non-spatial. For example, in a sample of 100 trees,

the filter does not discriminate between a fire year recorded by nine widely

dispersed trees from a date recorded by nine trees in a cluster. Filtering would

tend to reduce the number of fire years in a population, and thus decrease the

upper asymptote of a collector’s curve.

The event-area relationship has many potential applications in fire and forest management (Baker 1989). In ecology, scaling relationships have important

implications for forest demography and stand dynamics. In southwestern pine

forests, both mortality and scarring of survivors from fire events is strongly

age- and size-dependent. Individual trees are affected only by fires that are

proximate enough to generate the threshold values of exposure time and cambial temperature; fires that are too far away would have little effect. Thus, to

understand the regulatory influence of fires of forest demography, we must

evaluate fire occurrence (as well as fire-free intervals) at the “tree-scale.” While

much further research is required to define the radius of effect for fires on

seedlings and saplings, the spatial domain of demographically effective surface

fires is undoubtedly closer to 0.05 ha than to 500 ha. A fire occurring 1,500

m away probably has no demonstrable effect on the survival, growth, or reproduction of a target tree, whereas a fire within 50 m is likely to affect all

three demographic parameters. Scale dependence in the fire regime is the key

to understanding the spatial aspects of forest demography.

The event-area relationship can also be used as the basis for a measure of

spatio-temporal synchrony of events. Independent fire events can be synchronized regionally by climate, particularly periodic events such as El Niño

Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).

(Swetnam and Betancourt 1990; Swetnam and Betancourt 1992; GrissinoMayer and Swetnam 2000; Heyerdahl, Brubaker, and Agee 2002). When events

are synchronous across the landscape, fires that occur anywhere occur everywhere, leading to a collector’s curve that immediately reaches the asymptote.

The corresponding MFI-area function will have a flat (approaching 0) slope,

because as sample area increases, few events are encountered that have not

already been detected. By contrast, during periods when fires are not synchronized regionally, the collector’s curve rises more gradually, and the MFI-area

function declines at a faster rate because the landscape is dotted with small,

un-correlated events. We propose that the slope of the interval-area relationship may provide a statistical measure of regional entrainment of the fire regime

by climate, and a potential indicator of regime shifts.

In terms of forest management, understanding the scaling relationships of

the natural fire regime can be a powerful tool for restoring natural or prescribed fire intervals in fire management programs (Allen et al. 2002). In a

simplistic example, fire intervals from data collected at the 100 ha scale (a

common extent for fire history samples) might be applied uniformly across

the landscape down to the level of individual tree clusters. The negative slope

of the MFI-area relationship shows, however, that smaller areas experience

fires less frequently than do larger areas. Scaling rules can help to make prescribed natural fire programs more realistic in their application.

The non-zero slope of the MFI-area function also suggests the importance

of reporting search area as an integral element of fire interval statistics. Because the mean fire interval is non-stationary over area, sample area should be

reported explicitly for a particular sample area (e.g., “7.5 yr for 100 ha”). The

use of area-corrected units should become standard practice to avoid confusion in interpretation of interval data in research and management alike.

Scaling Rules and Probability Models for Surface Fire Regimes in Ponderosa Pine Forests Falk and Swetnam
314 USDA Forest Service Proceedings RMRS-P-29. 2003.

Acknowledgments

Our thanks to Craig Allen, Chris Baisan, Kay Beeley, Peter Brown, Wally

Covington, Brian Enquist, Emily Heyerdahl, Kiyomi Morino, Brian McGill,

Robert Steidl, Will Turner, Tom Veblen, and Peter White for thoughtful reviews and contributions during preparation of this paper. Peter Fulé provided

a detailed review that strengthened the paper. We gratefully acknowledge assistance and cooperation of the Jemez Ranger District of the Santa Fe

National Forest, research funding from the Joint Fire Science Program and

the U.S. Forest Service Rocky Mountain Research Station, and support to

DAF from the Marshall Foundation and the University of Arizona Department of Ecology & Evolutionary Biology.

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USDA Forest Service Proceedings RMRS-P-29. 2003. 319

Uncertainty in Fire History and Restoration of

Ponderosa Pine Forests in the Western United

States

William L. Baker1 and Donna S. Ehle1

Abstract—Fire-history data for ponderosa pine forests in the western U.S. have uncertainties and biases. Targeting multiple-scarred trees and using recorder trees when

sampling for fire history may lead to incomplete records. For most of the western

U.S., research is insufficient to conclude that high-severity fires did or did not occur

in these forests prior to EuroAmerican settlement, because the needed data are not

commonly collected. The composite fire interval is shown here to be misleading,

but this can be remedied in part with interval estimates by fire size class. These

problems mean that an assumption—that high surface-fire frequencies will restore

and maintain the structure of these forests—lacks a foundation in reliable fire-history

research.

Introduction

Restoration of fire in ponderosa pine forests depends upon fire-history

data that are potentially biased and more uncertain than generally recognized (Minnich et al. 2000, Baker and Ehle 2001). Problems include a lack

of modern calibration, inappropriate measures, targeted sampling, absence of

fire-severity evidence, and insufficient treatment of variability and uncertainty

(table 1). Some of these problems may be resolved quickly, while others will

require longer study or may never be resolved. Here we highlight a few of the

problems, suggest some remedies, and provide some thoughts regarding restoration of fire, given these problems.

No Modern Calibration

A significant problem plaguing fire-history research is a lack of modern

calibration. Pollen studies, fire-history studies, and other paleo-ecological studies require calibration to determine whether evidence is preferentially preserved

or lost and how it can be interpreted. Little is known about how fires leave

evidence in the landscape over time. There is no way of knowing, without

observing actual fires over time, whether it is possible to accurately reconstruct parameters (e.g., mean fire interval) of the fire regime from fire scars,

and, if so, how to sample to best accomplish this. Calibration may allow corrections to be derived that enable reasonably accurate reconstructions.

One calibration approach might be to use fire boundaries reconstructed

using aerial photographs (e.g., Minnich et al. 2000) or use other historical

records, such as atlases of past fires. This would be particularly valuable if

multiple approaches to sampling on the ground were compared to aerial-photo

1Department of Geography and Recreation,

University of Wyoming, Laramie, WY.
320 USDA Forest Service Proceedings RMRS-P-29. 2003.

or map estimates. However, photographs and historical sources also have limitations and biases. Small fires may be undetectable in typical aerial photographs,

and dating to single years is usually not possible (Minnich et al. 2000). There

is no research program at the present time to actually undertake this calibration work, but it is surely needed.

In lieu of calibration, all that can be done is to work with sampling designs,

sample sizes, and analysis techniques to see how the sampling estimates vary

relative to a more complete sample. Some of this relative comparison work has

been underway (Baker and Ehle 2001), but even this work is in its infancy.

New sampling designs are being proposed and studied (e.g., Arno et al. 1993,

Heyerdahl et al. 2001). There are promising signs that in a few years we will

know how to sample in the most efficient, unbiased manner.

Potential Biases and Uncertainties

Targeting Multiple-Scarred Trees

Fire-history researchers have seldom sampled randomly or in an unbiased

manner. Instead, they typically and purposely seek trees containing multiple

scars and places that contain high scar densities (table 1). These are assumed

to increase the length of the record and maximize identification of the fires

that burned a stand. However, no study has actually compared the fires identified through targeting with those on non-targeted trees, or examined the

effects of targeting on estimates of fire intervals in ponderosa pine forests.

To compare how targeted and non-targeted trees record fires and fire intervals, we sampled all visible scars on trees in nine plots randomly placed within

the ponderosa pine zone in Rocky Mountain National Park (Ehle and Baker,

in press). A total of 137 scarred trees was sampled. All fire scars were visually

crossdated using a master chronology. Most trees had a single fire scar, but six

trees had four or more scars per tree (figure 1). Trees with four or more scars

are those that typically would have been selected for sampling using a targeting approach, based on a review of ponderosa pine fire histories (Baker and

Ehle 2001). These six trees contained a total of 35 fire scars. We randomly

selected an equal sample of 35 scars from trees that would not have been

Baker and Ehle Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States

Table 1—Some limitations, potential biases, and uncertainties in fire-history studies in ponderosa

pine forests.

No modern calibration

Only know that some historical fires can be detected

Biases

Targeted sampling

Trees with multiple fire scars

Places with high fire-scar densities

Old trees or forests with long fire records; avoid young trees and forests

Trees with open scars

Fire severity unstudied, but assumed to be low

Necessary age-structure data not collected

Analysis and treatment of fire-scar data

Recorder trees-do they work?

Only scar-to-scar intervals included

Compositing is biased toward smaller fires

Uncertainties

Fire perimeters unknown

Fire record is uncertain due to unrecorded fires and unburned area within fire perimeters

Variability in fire-intervals is large and seldom explicitly treated

Large variability means sample sizes provide insufficient power for comparisons

Bracketing and confidence intervals are warranted
USDA Forest Service Proceedings RMRS-P-29. 2003. 321

targeted (trees containing £3 scars). A third sample of 35 scars was obtained

from single-scarred trees. Individual trees did not occur in more than one of

these samples.

Then, we separated the fires that were identified by these scars into five

combined size and severity classes (figure 2; see also Ehle and Baker, in press).

Low-severity fires leave numerous surviving trees, while mixed-severity fires

leave only a few survivors in a plot, or are high-severity fires in part of a landscape and low-severity elsewhere (Ehle and Baker, in press). Small fires in this

study scar more than one tree, and are not known to have spread beyond a

50 m X 50 m plot, but could have been as large as 1.2 km2

. Large fires burned

>1.2 km2

.

The targeted sample identified more fires (n = 29) than did the singlescarred trees (n = 20) or the non-targeted sample (n = 16) even though the

Figure 1–Percentage of sampled, firescarred trees (n=137) that have one

or more than one scar per tree. The

number of trees is listed above each

bar.

Figure 2–Effects of targeted sampling

on the number of detected fires for

fires of different sizes and severities.

Small fires likely do not exceed the

area of a sampling plot (50 m X 50

m), while large fires burn > 1.2 km2

.

Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States Baker and Ehle
322 USDA Forest Service Proceedings RMRS-P-29. 2003.

number of scars was 35 in all cases. The fires identified by the samples can be

compared to the total set of 60 fires identified by the 137 scarred trees in the

nine sampled plots (“Whole Dataset” in figure 2). The targeted sample generally

identified more of the small fires affecting only one tree and the small, lowseverity fires, while the non-targeted sample and single-scarred trees identified

few one-tree fires, but did as well or slightly better at identifying large,

low-severity fires and mixed-severity fires (figure 2). Seventeen one-tree fires

occurred in the nine plots (each of 0.25 ha) over a period of about 300 years,

which is a rate of about one tree/ha scarred by fire every 40 years, an insignificant amount. If one-tree fires are ignored, there is not much difference among

the samples in ability to detect fires of different size and severity.

However, an important difference is that the targeted sample comes from

only six trees, while the single-scarred sample comes from 35 trees. Less effort

is required to obtain the 35 scars from only six trees than from 35 singlescarred trees. However, 35 trees provide a much better spatial sample of where

the fires burned, thus making it possible to more correctly identify fire size

and severity (if age-structure data are also collected). If 35 trees can be sampled

in either case, many more fires will be detected with a targeted sample of trees

containing >4 scars than with a sample of single-scarred trees.

In our review (Baker and Ehle 2001), we expressed concern that fire intervals identified in a targeted sample might be much shorter on average than in

a non-targeted sample. To test this, we used the same sets of samples from

targeted, non-targeted, and single-scarred trees, each sample containing 35

fire scars. Then, we listed all the fires and fire intervals identified by each

sample of 35 fire scars, and used an ANOVA (done using Minitab 12.1; Minitab,

Inc. 1998) to test the null hypothesis that the mean fire interval for small,

low-severity fires is equal regardless of sampling technique. While fire-interval

data can have non-normal distributions, parametric statistical tests remain valid

(Johnson 1995). We repeated the ANOVA for large, low-severity fires. Comparisons for mixed-severity fires are not possible due to small sample sizes

(figure 2). The null hypothesis cannot be rejected for small, low-severity fires

(F = 0.21, p = 0.810) or large, low-severity fires (F = 0.00, p = 0.997).

While the sample from multiple-scarred trees may not be biased in this

regard, multiple-scarred trees alone will not identify all the fires in a stand.

Three of the 60 fires were only found on single-scarred trees, five were only

on double-scarred trees, and three were only on triple-scarred trees, all of

which would be missed if trees containing four or more scars were targeted.

Of these 11 fires (18% of the 60 fires), two were one-tree fires (figure 2), but

eight were small, low-severity fires, while one was a significant high-severity

fire. Three of these 11 fires occurred near or before AD 1700 and documented 30% of the 10 ancient fires found in the study area. Researchers seeking

complete fire histories or long fire histories will miss important fires and ancient fires if only multiple-scarred trees are sampled, at least in this study area.

We conclude that targeting multiple-scarred trees in this case study does

not produce a biased estimate of the fires that occurred in a larger sample or a

biased estimate of the mean fire interval relative to that found with other

samples. However, fire histories derived from targeted sampling may be

incomplete, particularly missing some important fires and ancient fires.

However, this one small study is insufficient to draw strong conclusions

about targeting. Fire intervals in this case study are quite variable, and the

test, as a result, may not have much statistical power. Further testing is needed

before these results are applied elsewhere. The other potentially significant

targeting biases (Baker and Ehle 2001) also need testing. Moreover, until

there is a modern calibration, the possibility remains that these sampling

Baker and Ehle Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States
USDA Forest Service Proceedings RMRS-P-29. 2003. 323

approaches simply produce equally biased estimates of fire intervals and other

parameters of fire regimes.

Crown Fires and Mixed-Severity Fires Not Sampled in

Ponderosa Pine Forests

If restoration of fire in ponderosa pine forests is to be successful, historical

variability in fire severity must also be known. The evidence needed to determine fire severity is a combination of fire-scar data and age-structure data near

each scar. Low-severity fires generally lead to low mortality of larger, established trees. High-severity fires can lead to pulses or a cohort of post-fire

regeneration (Ehle and Baker, in press). A mixed-severity fire has a high-severity, crown-fire component and an associated low-severity component.

A fire scar alone, or even multiple fire scars across a landscape, reveal little

about the severity of the fire. Fire scars indicate only that a fire was on the

surface at the scarred tree itself. This tree could be a lone survivor of a fire that

was in the crown of every other tree in the surrounding landscape. Scattered

surviving trees are not uncommon in crown-fire landscapes (e.g., Kipfmueller

and Baker 1998). The fire may have also have been mixed-severity, burning

on the surface over a part of the landscape where the scar was found, and then

crowning out in patches (e.g., Huckaby et al. 2001).

The idea that surface fires predominate in ponderosa pine forests has been

so pervasive that fire-history researchers commonly study fires in these forests

without collecting age-structure data, then erroneously conclude that it is

known that surface fires predominate or that crown fires did not occur. Some

researchers have even implied that, if fire-scars are present and ponderosa pine

is present, this indicates that the fire regime sustained only low-severity surface fires (Heyerdahl et al. 2001). This is false, as crown fires in ponderosa

pine forests can be followed within a few decades by surface fires as the stand

develops (Ehle and Baker, in press).

Thirty-nine studies constitute nearly all the published scar-based fire-history research on pure ponderosa pine forests in the western United States

(Baker and Ehle 2001). Only nine of the 39 collected the age-structure data

needed to determine whether fire severity was low, medium, or high (table 2).

Four other studies collected age structure, but not fire-scar data. These 13

studies with age-structure data reveal three general patterns. First, some studies of small areas or plots reveal an uneven age structure, often with apparent

pulses of regeneration separated by gaps in regeneration, suggesting an absence of crown fires. Regeneration pulses in these plots are sometimes linked

to variations in surface-fire frequency (Arno et al. 1995, 1997; Morrow 1986)

or a combination of fire and climate (Cooper 1960), or they cannot presently

be explained (Mast et al. 1999, White 1985). Second, some plots contain an

even age structure, characterized by large pulses of regeneration commencing

after a date identified on a nearby fire scar, suggesting a crown fire at the level

of the plot (Arno et al. 1995, 1997; Mast et al. 1998). Brown and Sieg (1996)

thought that ages of scarred trees in one plot were roughly synchronous, suggesting a possible crown fire or a climatic event. Age data (apparently collected

but not presented) suggest that infrequent stand-replacing fires occurred in

some parts of two study areas prior to EuroAmerican settlement (Barrett 1988,

Swetnam and Baisan 1996b).

Third, more extensive landscape-scale studies that include multiple plots

across an area of a few thousand hectares have revealed a mixed- or highseverity fire regime in the pre-EuroAmerican era. This was found in pure

ponderosa pine landscapes of Rocky Mountain National Park, Colorado (Ehle

Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States Baker and Ehle
324 USDA Forest Service Proceedings RMRS-P-29. 2003.

Table 2–Evidence of mixed-severity and high-severity (crown) fires in the pre-EuroAmerican period from studies of ponderosa pine

fire history and age structure in the western United States.

Fire

Age Historical scar

dataa datab data Comments on crown fires

Northwestern U.S.

Bork 1984 No No Yes No

Heyerdahl 1997 No No Yes They did not occur because surface fires did occur.

Morrow 1986 Yes No Yes No, uneven age structure with pulses of regeneration linked to low fire

frequency

Sherman 1969 No No Yes No

Soeriaatmadja 1966 No No Yes Yes, they probably occurred on higher elevation, more moist sites

Weaver 1943 No Yes No Yes, direct observation of even-aged stands suggesting past crown fires.

Northern Rockies

Arno 1976 No No Yes No

Arno and Petersen 1983 No No Yes No

Arno et al. 1995 Yes No Yes Yes, one stand of six dry-site stands and some wet-site stands

Arno et al. 1997 Yes No Yes Some dry-site ponderosa pine forests must have experienced

occasional stand replacement fires

Barrett 1988 Yes No Yes Yes, infrequent stand-replacing fires are possible in upper elevations

Freedman and Habeck 1985 No Yes Yes Yes, early historical observations suggest they occurred

Steele et al. 1986 No No Yes Yes, hypothesizes that they occurred in the past during periods of drought

and high winds.

Black Hills

Brown and Sieg 1996 Scars No Yes Yes, they were possible, but not verified; climate an alternative cause of

regeneration events

Brown and Sieg 1999 No No Yes No

Brown et al. 2000 No No Yes No

Shinneman and Baker 1997 No Yes No Historical records document large stand-replacing fires,

particularly in the moister northern Black Hills

Southern Rockies

Brown et al. 1999;

Kaufmann et al. 2000;

Huckaby et al. 2001 Yes No Yes Yes, 71% of sampled polygons had stand-replacing fires

Brown et al. 2000 No No Yes No

Ehle 2001; Ehle and Baker,

in press Yes No Yes Yes, in 6 of 9 plots

Goldblum and Veblen 1992 No No Yes Yes, but only in post-settlement

Laven et al. 1980 No No Yes No

Mast et al. 1998 Yes No Yes Even-aged cohorts and post-fire pulses of establishment, but linked to

gaps or spot fires (crown fires)

Rowdabaugh 1978 No No Yes No

Skinner and Laven 1982 No No Yes No

Veblen and Lorenz 1986, 1991 Yes Yes No Age structures and early photographs that show crown fires that occurred

near or before EuroAmerican settlement

Veblen et al. 2000 No Review Yes Yes, early photographs show them, and fire intervals are long enough to

allow them at higher elevations

Southwestern U.S.

Cooper 1960 Yes Yes No No evidence of crown fires except possibly on a part of the Prescott

National Forest

Dieterich 1980a No No Yes No

Dieterich 1980b No No Yes No

Dieterich and Hibbert 1990 No No Yes No

Fule et al. 1997 No No Yes No

Grissino-Mayer 1995 No No Yes No

Madany and West 1980 No No Yes No

Mast et al. 1999 Yes No No Same site studied by White (1985); uneven age structure with pulses of

regeneration not clearly linked to either climate or fire.

McBride and Jacobs 1980 No No Yes No

McBride and Laven 1976 No No Yes No

Morino 1996 No No Yes No

Savage 1989; Savage and

Swetnam 1990 No No Yes No

Stein 1988 No No Yes No

Swetnam and Baisan 1996a No No Yes No

Swetnam and Baisan 1996b Yes No Yes Yes, some evidence in dates of tree mortality and tree recruitment relative to

fires synchronous over large areas

Swetnam and Dieterich 1985 No No Yes No

Touchan et al. 1995 No No Yes No

Touchan et al. 1996 No No Yes No

White 1985 Yes No No No, uneven age structure with pulses of regeneration

aSufficient tree age data to be able to identify a crown fire in the pre-EuroAmerican period.

bEarly photographs or historical observations from near or before settlement by EuroAmericans.

Baker and Ehle Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States
USDA Forest Service Proceedings RMRS-P-29. 2003. 325

and Baker, in press) and in mixed-conifer landscapes with considerable ponderosa pine dominance at Cheesman Lake, Colorado (Brown et al. 1999,

Kaufmann et al. 2000, Huckaby et al. 2001). In the Rocky Mountain National

Park study, six of nine plots had stand-replacing fires and another plot had a

stand-replacing event caused by an unidentified agent (Ehle and Baker, in

press). In the Cheesman Lake study, 71% of sampled polygons had stand- replacing fires (Huckaby et al. 2001). Fires in both landscapes often were

mixed-severity at the landscape scale, burning as surface fires in some areas

and then crowning over other areas. Both studies reported that smaller parts

of these landscapes contained uneven-aged stands with no evidence of crown

fires for the past few hundred years.

Studies that use historical records or early photographs also found that crown

fires occurred in some ponderosa pine forests, but not others, prior to

EuroAmerican settlement (table 2). Shinneman and Baker (1997) reviewed

historical evidence of extensive crown fires in the moister parts of the Black

Hills, and Freedman and Habeck (1985) also noted historical evidence of

crown fires prior to EuroAmerican settlement in a valley in western Montana.

In early historical photographs Veblen and Lorenz (1991) could see ponde- rosa pine landscapes that were burned in stand-replacing fires some time before

EuroAmerican settlement. Cooper (1960) reported that a review of early

literature failed to find evidence of crown fires in ponderosa pine forests in

Arizona before 1900, except on part of the Prescott National Forest. There is

no further explanation of the Prescott case. Weaver (1943 p. 9), describing a

broad region in the Pacific Northwest, simply stated that “extensive evenaged stands of ponderosa pine can probably be accounted for by the past

occurrence of severe crown fires, by severe epidemics of tree-killing insects...or

by the occurrence of extensive windthrows...” A more extensive review of

early historical reports and photographs might reveal where stand-replacing

fires had or had not occurred prior to EuroAmerican settlement.

For most of the ponderosa pine forests of the western United States there

are no data at all that would allow a determination of whether crown fires or

mixed-severity fires were present or absent before EuroAmerican settlement,

or have increased or decreased. Where studies have been done or historical

data were examined, crown fires or mixed-severity fires were sometimes found

and sometimes not (table 2). There is a hint in these data that crown- or

mixed-severity fires may occur on moister sites in the ponderosa pine zone.

No one, in any study anywhere in the West, has yet estimated how frequent

crown- or mixed-severity fires were in ponderosa pine forests, how large these

fires may have been, or what the fire rotation for these fires might have been

prior to EuroAmerican settlement. The data are perhaps there to allow this

estimation for study sites at Cheesman Lake, Colorado (Huckaby et al. 2001)

and in Rocky Mountain National Park (Ehle and Baker, in press). These study

areas, however, are small relative to the size of some recent fires (e.g., Hayman

Fire, 2002). Larger areas have been logged or burned, destroying the evidence of past fires. It may be difficult or impossible to determine whether

large, high-severity fires did or did not occur in ponderosa pine landscapes

prior to EuroAmerican settlement.

Given the lack of data, there is little basis for the general perception that

high- or mixed-severity fires, such as the 2000 fire that burned into Los Alamos,

New Mexico, are not natural in ponderosa pine forests (Allen 2002). The

conclusion that a particular fire is unnaturally severe is premature given the

absence of the necessary data. For nearly all the ponderosa pine forests in the

western United States it would also be premature to suggest that treatments

that lower the probability of crown fire or high-severity fire or lower fire risk

Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States Baker and Ehle
326 USDA Forest Service Proceedings RMRS-P-29. 2003.

are “restoration.” For most of the range of ponderosa pine in the West it is

not yet known whether these kinds of fires were or were not a part of the preEuroAmerican fire regime. Where crown fires occurred, thinning may be an

inappropriate restoration technique, just as it is inappropriate in some pinyonjuniper woodlands (Romme et al., this volume). In some cases, restoration

might even require reintroduction of high-severity fires, if they were unnaturally suppressed.

Analysis and Treatment of Fire-Scar Data

Recorder Trees—Do They Work?

It has long been thought that until a tree receives a fire scar, it is a poor

recorder of fires. Thus, fire historians often do not consider a stand to be

generally capable of recording the fires that occur in a stand until after some

number of trees has received a first scar (e.g., 3; Grissino-Mayer 1995). The

idea of a previously scarred “recorder tree” is that if there is an open scar,

subsequent fires should be more effectively recorded than if fires must produce the first scar. If recorders work, fires should show up more often on

recorder trees than as a first scar.

In our complete sample from 137 scarred trees, we found 60 fires. Nineteen of these fires (31.7%) show up only as first scars, while 17 fires (28.3%)

show up only as scars after the first scar (i.e., on recorder trees). This result

could occur if previously scarred trees are actually no better recorders or if

different fires affected the recorder trees and the trees with first scars. However, 24 fires (40%) show up as a mixture of first scars and scars on recorder

trees. Ninety-six of the 154 total scars (62%) documenting these 24 fires are

first scars while only 58 of the 154 scars (38%) occur on recorder trees. A

chi-square test leads to rejection of the null hypothesis that recorder trees and

trees without scars are equal recorders of fires when fires show up on both

( c 2 = 4.761, p = 0.029). In our study area, previously scarred trees are poorer

recorders of fire than are unscarred trees. Previously scarred trees do not perform as commonly expected, perhaps because multiple factors influence whether

a fire produces a scar. Smaller trees, for example, typically have thinner bark,

which offers less resistance to scarring, perhaps making them better recorders

than are larger trees. Our results suggest that if a complete history is desired,

fire-history data should be collected and used whether a tree is or is not previously scarred. Fire-history studies that only use recorder trees may miss a

significant part of the fire history.

Which Intervals Should Be Used?

Fire historians nearly always have focused on scar-to-scar (SS) intervals recorded on trees, omitting the interval between tree origin and the first scar

(OS interval; Baker and Ehle 2001) as well as the interval between the last scar

and tree death or the present. Yet, the OS interval estimates the real fire-free

interval needed for trees to reach a size sufficient to survive surface fires (Baker

and Ehle 2001). Since the OS interval does not necessarily begin with a fire,

the real fire-free interval may be underestimated by the OS interval.

The OS interval is typically longer than the SS interval (Baker and Ehle

2001). In our sample of 137 fire-scarred trees from Rocky Mountain National

Park’s ponderosa pine zone (figure 3), the pre-EuroAmerican OS interval on

individual trees (n = 71) has a mean of 55.4 years and an estimated median of

51.5 years. The pre-EuroAmerican SS intervals on individual trees (n = 40), in

Baker and Ehle Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States
USDA Forest Service Proceedings RMRS-P-29. 2003. 327

contrast, have a mean of 33.3 years and an estimated median of 28.5 years.

The estimated difference in means is 22.1 ±13.2 years (95% confidence interval). The regression equation in Baker and Ehle (2001) for estimating the OS

interval, if only the SS interval is known, suggests that the mean OS interval

would be 53.5 years for a mean SS interval of 33.3 years, reasonably close to

the 55.4 years actually found. The OS interval should be included as a real fire

interval, and including it generally lengthens the estimated mean fire interval

by about 1.6 times (Baker and Ehle 2001).

Compositing Biased Toward Small Fires

The mean “Composite Fire Interval” or CFI (Dieterich 1980a) is the traditional measure of central tendency in fire intervals, but this measure is flawed

as a general measure of the fire regime (Baker and Ehle 2001). One problem

is that the CFI pools fires of different extent and frequency. Regardless of the

real mean fire interval in a landscape, the mean CFI decreases as the number

of sampled fire-scarred trees and sampled area increase (Arno and Petersen

1983). The reason is that the numerous fires that scar only one tree (e.g.,

figure 2) are counted the same as an infrequent fire that scars many trees

(Minnich et al. 2000, Baker and Ehle 2001). By adding sampling area or

sampled trees, one quickly adds these apparently small fires. As a result, a CFI

can be interpreted as mostly reflecting the frequency of small fires that affect

little of the landscape.

A remedy for this shortcoming of a CFI is to analyze and report fire intervals separately for individual classes of fire size. Laven et al. (1980) may have

been the first to use this approach for ponderosa pine forests when they reported separate intervals for small fires and large fires. Bork (1984) showed

means and standard errors for fires varying in size from 1 plot to 5 plots

(figure 4). Morino (1996) calculated separate fire-interval distributions and

descriptive parameters (e.g., mean) for small fires, medium fires, and large

fires. Mean fire intervals for larger fires in ponderosa pine forests are 41.7

years (Laven et al. 1980), 60-150 years (Bork 1985 and figure 4), and 24.4

years (Morino 1996), while the corresponding mean fire intervals for small

fires in these studies are 20.9 years, 5.25 years, and 2.7 years, respectively.

Thus, larger fires in these cases have mean intervals that are 2-10 times as long

as are mean intervals for smaller fires. These estimates are imprecise, but illustrate that the mean fire interval for the fires that do most of the work in

ponderosa pine forests is much longer than suggested by typical CFIs.

Figure 3–Distribution of preEuroAmerican fire-scar intervals

for individual trees from a sample

of 137 fire-scarred trees in

ponderosa pine forests of Rocky

Mountain National Park.

Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States Baker and Ehle
328 USDA Forest Service Proceedings RMRS-P-29. 2003.

Our review of 11 studies in the western United States show that about 50%

of known fires are documented by a scar on only one tree (Baker and Ehle

2001). Given that these fires affect little land area, but dominate the CFI, we

particularly suggest that the frequency of one-tree fires should be reported

separately.

The idea that there is value in reporting intervals for fires of different sizes

underlies the now-popular reporting of interval data for all fires compared to

those that scar >10% or >25% of recorder trees (Grissino-Mayer 1995). However, it is not progressive restriction (sizes exceeding a certain size) that is

needed, but separate reporting of intervals for each size class. Reporting a CFI

for study areas of increasing size (e.g., Brown et al. 1999) is also not what is

needed, as it is well known that CFIs decrease as study area size increases, even

if the fire regime is the same across scales (Arno and Petersen 1983).

Separating fire-intervals by fire size also allows estimation of the fire rotation, a fundamental measure of the fire regime (Minnich et al. 2000, Baker

and Ehle 2001). Data on the relative frequency and importance of fires of

different sizes are invaluable for fire managers, as this information can be used

directly in prescribed burning plans, regardless of the size of the management

area. This is not the case for the traditional CFI, which is heavily dependent

on the size of the study area in which the CFI was calculated (Arno and Petersen

1983, Baker and Ehle 2001).

Figure 4–Mean return interval for fires of different size from three sites in eastern Oregon,

estimated by proportion of plots having at least two fire-scarred trees. Reproduced from Bork

(1985) Figure I-24 with permission from Joyce L. Bork.

Baker and Ehle Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States
USDA Forest Service Proceedings RMRS-P-29. 2003. 329

What are appropriate fire size classes to use? Even reporting intervals based

on number of affected plots (figure 4), or linear distances between plots, would

be an improvement over a traditional CFI. Size classes used by the U.S. Forest Service and other agencies would be advantageous, as data from fire-history

studies could then be compared to contemporary data from monitoring programs. Where fire-history data are insufficient to make fine distinctions in fire

size, pooling of adjacent categories would still allow useful comparisons with modern

data, particularly if small fires are segregated from large fires.

Unfortunately, it is difficult to estimate the size of surface fires using fire

scars. Grid-based or random sampling methods are increasingly making it

possible to approximate fire extent (Arno et al. 1993, Heyerdahl et al. 2001,

Morino 1996). However, there is not yet a calibration to guide correction of

size estimates from spatial sampling networks or sufficient study of appropriate spatial sampling designs for detecting fire sizes. Until this calibration and

sampling design work is done, a method to bracket the potential uncertainty

associated with assigning fires to size classes is needed.

Uncertainties

Fire intervals vary, and this variability is often large within a single tree and

among all the intervals within a stand (e.g., figure 3). This variability suggests

that fire intervals are not predictable results of the time for fuel to build up

after a fire; fire intervals are shaped by the timing of weather that promotes

fine-fuel accumulations and the timing of droughts (Veblen et al. 2000). This

variability in fire intervals makes comparison of sets of fire intervals from different periods or different sites difficult, as sample sizes must be large to be

able to detect even 50% or 100% differences in mean with adequate statistical

power (Baker and Ehle 2001). However, few researchers have actually used

statistical inference, instead simply presenting the sample data. Previous evidence that fire intervals have changed over time or differ among sites may not

bear up under statistical analysis, except where the change is obvious, as when

fires appear to virtually stop near or after settlement (e.g., Savage and Swetnam

1990).

Fire-interval data also have uncertainty that comes from at least two sources–

unrecorded fires and unburned area within fire perimeters. There is presently

no method to estimate the magnitude of these sources of uncertainty in a

particular stand or area. Baker and Ehle (2001) thus suggest that all estimates

of mean or median fire intervals should be bracketed using the restricted

(>10% scarred) CFI and individual-tree mean fire intervals. However, if fire

intervals are reported separately by fire size, as we recommend here, then the

appropriate brackets for the estimate of mean fire interval for a stand are the

unrestricted composite and individual-tree fire intervals.

Implications for Restoration

Fire-history research methods are in need of reassessment, as traditional

measures are misleading or in error as sources of information useful for designing a program for restoring fire in ponderosa pine forests. The time that it

took for fire to burn through these forests prior to EuroAmerican settlement

is much longer than is implied by typical composite fire intervals, which have

been reported to be between 2-25 years (Baker and Ehle 2001). The large

fires, that actually account for most burned area, occur at intervals that are

several times longer than reported composite fire intervals. Baker and Ehle

Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States Baker and Ehle
330 USDA Forest Service Proceedings RMRS-P-29. 2003.

argued that the population mean fire interval in western ponderosa pine forests is instead more likely to lie between 22-308 years. However, until there is

a modern calibration and further testing of the potential biases and uncertainties we have identified, it would be premature to draw strong conclusions

about what the fire intervals were in pre-EuroAmerican ponderosa pine forests.

Our analysis suggests that repeated prescribed burning of large areas of

ponderosa pine forests at short intervals (e.g., less than 20 years) lacks a sound

basis in science, and should not be done at the present time if the goal is

restoration. In most parts of the western United States there is also insufficient evidence to support the idea that mixed- or high-severity fires were or

were not absent or rare in the pre-EuroAmerican fire regime. Thus, programs

to lower the risk of mixed- or high-severity fires in ponderosa pine forests

(e.g., the National Fire Plan, Laverty and Williams 2000) have insufficient

scientific basis if the goal is restoration.

Fire practitioners interested in restoration can certainly proceed with reintroducing fire into these forests on a limited basis, however. In many areas,

fire has been excluded by livestock grazing or intentional suppression for a

long period. We suggest that prescribed burning a large area once is not likely

to push the ecosystem outside its historical range of variability. Reintroduction of small prescribed fires that burn a single tree or a few trees in a landscape

is also appropriate, at least in our study area. However, prescribed burning of

large land areas after short intervals (e.g., <20 years) has little scientific basis at

the present time, if the goal is to restore the natural variability of the

pre-EuroAmerican fire regime.

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Province ecosystems: a symposium proceedings. Gen. Tech. Rep. RM-GTR-289,

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

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Swetnam, T. W.; Dieterich, J. H. 1985. Fire history of ponderosa pine forests in the

Gila Wilderness, New Mexico. In: Lotan, J. E.; Kilgore, B. M.; Fischer, W. C.;

Mutch, R. W., tech. coords. Proceedings—symposium and workshop on wilderness

fire. Gen. Tech. Rep. INT-182, Ogden, UT: U.S. Department of Agriculture,

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Touchan, R.; Swetnam, T. W.; Grissino-Mayer, H. D. 1995. Effects of livestock grazing

on pre-settlement fire regimes in New Mexico. In: Brown, J. K.; Mutch, R. W.;

Spoon, C. W.; Wakimoto, R. H., tech. coords. Gen. Tech. Rep. INT-GTR-320,

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Touchan, R.; Allen, C. D.; Swetnam, T. W. 1996. Fire history and climatic patterns in

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Veblen, T. T.; Lorenz, D. C. 1986. Anthropogenic disturbance and recovery patterns

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Veblen, T. T.; Lorenz, D. C. 1991. The Colorado Front Range: a century of ecological

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Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States Baker and Ehle
334 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 335

Ancient Piñon-Juniper Forests of Mesa Verde

and the West: A Cautionary Note for Forest

Restoration Programs

William H. Romme1, Lisa Floyd-Hanna2, David D. Hanna2

Abstract—Fuel reduction and fire mitigation activities may be linked to restoration

of overall forest health, but the two goals do not always coincide. We illustrate the

importance of understanding both historic and contemporary fire regimes by evaluating the piñon-juniper forests of Mesa Verde National Park in southwestern Colorado.

These dense forests are characterized by infrequent, severe fires occurring at intervals of many centuries. Stand structure, composition, and fire behavior have not

been substantially altered by 20th century fire suppression, in contrast to other piñonjuniper systems. We hypothesize that three qualitatively different disturbance regimes

characterize piñon-juniper ecosystems throughout the West.

Introduction

Amajor effort is under way to reduce the fire hazard in western forests

by thinning tree canopies, removing shrubs and coarse woody debris,

and conducting low-intensity prescribed burning. Often, these fuel reduction

or fire mitigation activities are linked to restoration of overall forest health

(e.g., Covington et al. 1997). This combination of pragmatic objectives (reducing fire hazard at the wildland-urban interface) and ecological objectives

(restoring ecological integrity and resilience) is quite sensible in forests that

were formerly characterized by frequent, low-intensity fires, and where twentieth century activities like logging, grazing, and fire suppression have

dramatically changed forest structure and function — e.g., in many western

ponderosa pine forests (Hardy and Arno 1996, Covington et al. 1997).

Indeed, the authors of this paper are currently involved in just such a program

of thinning and burning degraded ponderosa pine forests in southwestern

Colorado, both to achieve the pragmatic objectives of reducing fire hazard

and providing material for the local logging industry, and to achieve the ecological objectives of stimulating the suppressed herbaceous stratum and

providing habitat for cavity nesting birds and species that prefer open forest

conditions (Lynch et al. 2000).

We are concerned, however, that the distinction between fire hazard mitigation and ecological restoration may become blurred, with sometimes

unfortunate consequences for ecological integrity and resilience. In particular, because of pressures to meet treatment targets under the National Fire

Plan (targets that may be defined primarily in terms of acreage treated per

year), managers may be tempted to apply thinning and burning in forest systems where such treatments are unnecessary or even inappropriate from an

ecological standpoint (Cole and Landres 1996). To illustrate this concern, we

present a case study of the piñon-juniper forests of Mesa Verde National Park

in southwestern Colorado.

1Department of Forest Sciences, Colorado

State University, Fort Collins, CO.

2

Prescott College, Prescott, AZ.
336 USDA Forest Service Proceedings RMRS-P-29. 2003.

Piñon-juniper vegetation covers a vast area in western North America, and

exhibits a wide range of stand structures and dynamics (Wangler and Minnich

1996, Miller et al. 1999). Recent research in the Great Basin has demonstrated clearly that piñon and juniper are increasing in density throughout

much of this region. Many areas that were formerly dominated by shrubs are

now being taken over by piñon and juniper, a trend that began in the late

1800s and is continuing. The causes of piñon and juniper expansion include

livestock grazing, fire exclusion, and climatic changes (Miller and Wigand 1994,

Miller and Tausch 2001). Some piñon-juniper and juniper savannas in New

Mexico and Arizona also appear to have developed denser tree canopies in the

twentieth century, or the trees have invaded grassland areas (Jameson 1962,

Dwyer and Pieper 1967). Some former grasslands in Bandelier National Monument in northern New Mexico are now largely dominated by juniper and

piñon trees <150 years old (C. Allen, personal communication). Because of

these well-documented examples of piñon-juniper expansion that were clearly

caused by fire exclusion and other human activities, many managers throughout the West have tended to view the entire piñon-juniper vegetation type as

being degraded and in need of intensive restoration – usually by means of

chaining, roller-chopping, hydro-mulching, and/or burning.

What is often missing in plans for restoration of piñon-juniper vegetation is

recognition of the fact that throughout the West there are also ancient piñonjuniper stands with trees >400 years old (examples below). These stands were

already well developed even before the late 1800s, and should not be regarded as abnormal consequences of grazing, fire exclusion, and climate change.

Unfortunately, thinning and burning programs that would be appropriate and

effective in piñon-juniper stands that have developed abnormal tree density

during the last century, are being proposed or implemented in old-growth

piñon-juniper stands that probably need no restoration. The result of such a

well intentioned but misguided restoration effort is likely to be degradation of

a relatively rare vegetation type (i.e., old-growth piñon-juniper forest) that

contributes much to the biological diversity of western North America. As the

pace of fuel reduction and restoration programs accelerates under the

National Fire Plan, we have an urgent need to provide criteria for distinguishing

between piñon-juniper stands that need treatment and those that do not. We

believe that the situation we describe for piñon-juniper also applies to several other

important forest types where mitigation and restoration plans may be under way.

This paper has two objectives. First, we describe the fire history, stand structure, and natural fire regime in old-growth piñon-juniper forests of Mesa Verde

National Park (MVNP), as a case study to demonstrate that some piñon-juniper vegetation has not changed substantially in the last century, and therefore

is not in need of broad-scale thinning or burning to achieve ecological objectives. Second, we develop a set of general hypotheses for predicting where in

western North America we are most likely to find piñon-juniper vegetation

that has undergone abnormal successional changes in the last century as a

result of grazing, fire exclusion, and climate change, and where we are likely

to find old-growth piñon-juniper stands that have not been degraded and do

not require treatment to restore ecological integrity.

Fire History in Piñon-Juniper Forests of

Mesa Verde National Park

Mesa Verde is a large, prominent cuesta in southwestern Colorado composed of uplifted and deeply eroded Cretaceous sedimentary rocks (Griffits

Romme, Floyd-Hanna, and Hanna Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs
USDA Forest Service Proceedings RMRS-P-29. 2003. 337

1990). Mesa Verde National Park (MVNP) occupies the northeastern portion

of the cuesta. The remainder of the cuesta is mostly in the Ute Mountain

reservation and is environmentally similar to the park. The two most extensive

vegetation types are piñon-juniper forests (Pinus edulis and Juniperus

osteosperma), found mostly on the southern end of the cuesta at slightly lower

elevations, and Petran chaparral or oak-serviceberry shrubland (Quercus

gambellii and Amelanchier utahensis), found mostly on the northern end of

the cuesta at higher elevations. Floyd et al. (2000) mapped the major fires

since the 1840s in the shrubland portion of MVNP, based on post-fire cohorts of re-sprouting oak, and determined that the average fire return interval

is about 100 years. A graph of cumulative time-since-fire indicates little difference between late-nineteenth century and late-twentieth century area burned

per decade. Thus, the fire exclusion policy in effect since the Park’s establishment in 1906 probably has prevented many fires that would have remained

small if not suppressed, but had little impact on the large fires that ignited

under conditions of extreme drought and high wind. These latter kinds of

fires, uncontrollable even with modern fire suppression technology, are infrequent but account for most of the area burned in a century — a situation

similar to many boreal forests (Johnson 1992), subalpine forests (Romme

1982), and chaparral vegetation (Minnich and Chou 1997, Moritz 1997).

Floyd et al. (2000) presented preliminary data on fire history in the piñonjuniper forests of MVNP. We summarize those data here and augment them

with additional, unpublished information to provide a fuller picture of the fire

regime in the piñon-juniper forests. We structure our analysis around four

questions: (1) Are low-severity surface fires an important component of the

natural fire regime? (2) How frequent were fires of any kind before 1900?

(3) Have piñon and juniper invaded other vegetation types since 1900?

(4) Has tree density increased since 1900 in piñon-juniper stands?

Are low-severity surface fires an important component of

the natural fire regime?

The large fires of the twentieth century in MVNP have all been standreplacing crown fires, in both piñon-juniper and shrubland vegetation.

Low-severity surface fires have occurred only at the margins of the crown fires

as those fires were dying out. But were low-severity surface fires more common prior to Park establishment in 1906? We think not. Low-severity fires

often scar susceptible trees, and indeed, the resulting fire scars are the basis of

reconstructions of fire history in ponderosa pine and other forests characterized by frequent, low-severity fires. Despite explicitly searching for fire scars

throughout MVNP during our 10 years of field work, we have not found any

– aside from two trees having single basal scars that could be from fire but

probably are from other causes. Piñon and juniper do not form the best scars

for dendrochronological dating purposes, but they can be scarred by lowintensity fires (Tausch and West 1988). Moreover, numerous small patches of

ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) exist

within and adjacent to the piñon-juniper forests, but these excellent recorders

of low-severity fire also lack fire scars in MVNP. Given the paucity of fire scars,

we conclude that low-severity surface fires simply were never extensive in piñonjuniper forests of MVNP.

However, we find ample evidence that stand-replacing crown fires occurred

in the piñon-juniper forests even before 1900. Figure 1 is a photograph taken

in 1934 (from MVNP archives) of an area that burned at some unknown time

prior to Park establishment in 1906. The standing snags and the sharp border

Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs Romme, Floyd-Hanna, and Hanna
338 USDA Forest Service Proceedings RMRS-P-29. 2003.

of the unburned forest indicate clearly that this was a stand-replacing crown

fire. From our fire history map (Floyd et al. 2000) and the general location of

the photograph, we suspect that the fire occurred in the 1880s – some 50

years before the photo. Indeed, the scene looks much like areas today that

were burned in the middle part of the twentieth century.

We also find patches of charred juniper snags within the piñon-juniper forests of MVNP. We took increment cores from piñon trees growing beneath or

adjacent to the charred snags in one such patch on Chapin Mesa, known locally as “the glades.” The living trees that we sampled were so close to the

snags that they could not possibly have survived the fire that created the snags.

Some of the sampled piñons were >200 years old, indicating that the fire

occurred in the 1700s or earlier (Floyd et al. 2000). This finding also indicates

that evidence of past fires (in the form of charred snags) can persist for a very

long time in this semi-arid environment.

How frequent were fires before 1900?

By measuring the extent of piñon-juniper forest burned during the twentieth century, and considering that twentieth century fire suppression probably

did not have much effect on the few large fires that accounted for most of this

burned area, we determined that the mean fire interval in piñon-juniper forests of MVNP is approximately 400 years (Floyd et al. 2000). In a landscape

Figure 1—Photograph taken in 1934 in the western portion of Mesa Verde National Park, near an area that burned in that year

on Wetherill Mesa. The photo is not of the 1934 burn, but shows an area that was burned at an unknown time prior to Park

establishment in 1906. Fire history reconstructions (Floyd et al. 2000) suggest that the area in this photo probably burned in the

1880s. Note the edge of dense, unburned piñon-juniper forest in the background.

Romme, Foyd-Hanna, and Hanna Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs
USDA Forest Service Proceedings RMRS-P-29. 2003. 339

characterized by such long fire intervals, we would expect to find numerous

stands that had remained unburned for even longer periods. Indeed, much of

the piñon-juniper forest in MVNP contains no evidence of past fire whatsoever – no fire scarred trees, no charred snags, no charred logs. Tree ages (see

below) also indicate that many of the stands are very old. Based on a convergence of several lines of evidence (paucity of fire-scars and charred wood, very

old trees, and historic photographs – see below), we believe that much of the

piñon-juniper forest in MVNP has not been subjected to any major disturbance since the ancestral Puebloan people (who built the famous cliff dwellings)

abandoned the area 700 years ago.

Have piñon and juniper invaded other vegetation types

since 1900?

Young piñon and juniper appear to be slowly increasing in some of the

Park’s shrublands. The stands so affected are developing after fires that occurred in the mid to late 1800s (Floyd et al. 2000). Given the naturally long

intervals between fires in Mesa Verde’s shrublands (mean of ca 100 years),

this trend probably represents the natural course of post-fire succession rather

than a true “invasion” by trees (Erdman 1970).

We also see young piñon and juniper establishing in some stands of big

sagebrush (Artemisia tridentata) growing on small patches of deeper soils

within the piñon-juniper forests. This may represent actual “invasion,” since

the trees are mostly young (<100 years old). However, it does not appear to

be an anomaly caused by twentieth-century fire exclusion, because these areas

apparently burned rarely even before 1900 (above). The extent of the area so

affected within MVNP is very small.

Has tree density increased in piñon-juniper stands since

1900?

The piñon-juniper forests of MVNP are very dense in many places. Piñon

generally has more stems per hectare, but juniper has greater basal area (unpublished data). Small and presumably young piñon are especially noticeable

and give a visual impression of an ongoing increase in stand density.

However, old photographs from MVNP indicate that the piñon-juniper

forests have been very dense since at least the early part of the twentieth century. Figures 2 and 3, taken in 1929 and 1934, respectively (from MVNP

archives) show dense stands — much like what we see today. The stand depicted in figure 3 (from 1934) gives the impression of a two-tiered structure,

with a sparse canopy of older trees and a thick understory of younger trees.

However, without information on mortality rates in the understory, it is difficult to interpret long-term stand dynamics. We need detailed age structure

data to confidently interpret trends in stand structure. We currently have age

structure data derived from age-dbh regressions in one stand that appears

representative of the old forests at the southern end of MVNP (Floyd et al.

2000), and we are in the process of directly determining age structure via ring

counts in several additional stands. A plot of log(stems/ha) vs log(tree age) in

this one stand is approximately linear for both piñon and juniper (figure 4) —

a pattern associated with stable, old-growth forests that are not undergoing

any major successional trends (Leak 1975). The largest piñon in figure 4 are

>400 years old and the largest juniper are >600 years old.

We need additional detailed age distribution data to resolve the question of

increasing stand density in piñon-juniper forests of MVNP during the last

Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs Romme, Floyd-Hanna, and Hanna
340 USDA Forest Service Proceedings RMRS-P-29. 2003.

Figure 2—Photograph taken in 1929 of cliff dwellings in the southern portion of Mesa Verde National Park. Note the dense

piñon-juniper forest on the rim above the ruins, a forest that does not look much different from the dense forests of today.

century. The abundance of small and young trees in the forests today does

suggest that density has increased somewhat in the last 100 years. However,

the old photos indicate that the magnitude of increase has not been great.

And even if the forests have become more dense, the mechanism probably is

related to climatic changes, direct effects of increasing atmospheric CO2

, or

other causes (Miller and Wigand 1994)—but not to fire exclusion. Given that

these forests rarely burned before 1900 (as argued above), post-1900 fire

exclusion cannot be the primary reason for increasing tree density after 1900.

Reasons for the Distinctive Fire Regime in

Mesa Verde

The evidence just presented indicates that the natural fire regime in Mesa

Verde’s piñon-juniper forests is characterized by occasional tiny fires (often a

single lightning-ignited tree) but few fires of any significant extent. Most ignitions fail to spread, and much of the forest has not burned for at least 700

years. Large fires do occur periodically, however, under extreme weather conditions, and these rare fires are severe and stand-replacing. This fire regime

probably has been altered only slightly by twentieth century fire suppression

or other activities.

Romme, Floyd-Hanna, and Hanna Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs
USDA Forest Service Proceedings RMRS-P-29. 2003. 341

Why do piñon-juniper forests of MVNP have such infrequent large fires?

The reason is not a lack of fire starts: lightning ignites numerous fires every

summer in the Park’s piñon-juniper zone (Omi and Emrick 1980). However,

the vast majority of these fires burn no more than an individual tree or snag

before going out – with or without the intervention of a fire suppression crew.

Even though the piñon-juniper forests in MVNP support enormous fuel loads

of both dead and live material (Omi and Emrick 1980), the fuels are very

patchily distributed. Patches of heavy fuels (a few square meters in extent) are

separated by comparably sized patches of rock, bare ground, or sparse cover

of herbs that do not carry fire readily. Because of the lack of horizontal fuel

continuity, fire can spread from fuel patch to fuel patch only under conditions

of strong winds and extremely low fuel moisture. In these circumstances, fire

spreads through the crowns of live trees, generating extremely high energy

release and displaying great rates of spread (personal observations). The necessary weather conditions for such fire behavior occur in only a few years of

each decade, and coincide with local ignition only once every decade or two.

Once such a fire is started, however, it may cover a large area, going out only

when the wind dies down, rain falls, or the fire reaches an extensive area of

non-flammable terrain. The deep canyons of MVNP, bordered by tall cliffs

with poorly vegetated shale slopes at their bases, probably are significant

Figure 3—Photograph taken in 1934 in the western portion of Mesa Verde National Park, near an area that burned in that year on

Wetherill Mesa. The photo was taken to show the kind of forest that burned in that year. Note the high density of the stand in

1934, similar to the dense stands in this area today.

Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs Romme, Floyd-Hanna, and Hanna
342 USDA Forest Service Proceedings RMRS-P-29. 2003.

natural barriers to fire spread (figure 5). As one park ranger put it, “Fire in

MVNP tends to burn either one tree or one mesa” (S. Budd-Jack, personal

communication).

Heavy livestock grazing in the late 1800s and early 1900s altered fire regimes in many western forests by removing the grass and other fine fuels that

formerly carried frequent, low-intensity fires (e.g., Miller and Wigand 1994,

Covington et al. 1997). Heavy grazing also occurred in MVNP from around

1900 through the early 1930s (superintendents’ annual reports and photos in

MVNP archives). The reports are quite vague as to specific locations where

cattle were grazed. However, we think that nearly all of the heavy livestock

grazing occurred in the meadows and shrublands at the north end of the Park

—not in the piñon-juniper forests at the south end. The homesteads, windmills, and stock tanks were all located in the northern area, and all of the old

photos that we have located showing heavily grazed lands in MVNP depict

meadows and adjacent shrublands. Heavy grazing in the piñon-juniper forests

would have been problematic in any event because of lack of water on the dry

0

0.5

1

1.5

2

2.5

3

2.3 2.4 2.5 2.6 2.7 2.8 2.9

log (age)

Colorado Piñon

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

log (age)

Utah Juniper

Figure 4—A plot of log(stems/ha) vs.

log (tree age) for a stand of dense

piñon-juniper forest at the southern

end of Mesa Verde National Park.

The linear relationship for Juniperus

osteosperma (a) and for Pinus edulis

(b) indicates that both tree species

have an approximately stationary

age structure of the kind that is

associated with stable, old-growth

forests (Leak 1975).

Romme, Floyd-Hanna, and Hanna Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs
USDA Forest Service Proceedings RMRS-P-29. 2003. 343

mesa-tops and lack of forage. The heavily grazed meadows and shrublands

have regained their grass cover in the 65 years since grazing was terminated in

MVNP, but the old piñon-juniper forests still have only a sparse cover of Poa

fendleriana and a few other native bunchgrasses. We doubt that grass cover

was ever very great in the dry, shallow soils at the south end of Mesa Verde.

Even if there was somewhat more grass prior to 1900, it apparently did not

carry surface fires, because we find almost no fire scars (above).

Implications for Fire Management in

Mesa Verde National Park

MVNP contains one of the greatest concentrations of prehistoric cultural

sites and artifacts in North America. The Park was established in 1906 to

protect these archaeological treasures, and protection of cultural resources

remains the central mission of MVNP. Many of the most outstanding cultural

resources are located in the piñon-juniper forests at the southern end of the

Park and are vulnerable to damage or destruction by fire (Romme et al. 1993).

Recognizing this dangerous situation, MVNP has implemented a fire mitigation program that emphasizes mechanical thinning of dense forests in the

immediate vicinity of sensitive cultural resources.

Figure 5—Photo taken in the southern portion of Mesa Verde National Park in the 1930s. Note the dense piñon-juniper forests on

the mesa tops, which resemble the dense stands of today, and the cliffs and sparsely vegetated slopes that probably tend to prevent

fire spread from one mesa to another.

Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs Romme, Floyd-Hanna, and Hanna
344 USDA Forest Service Proceedings RMRS-P-29. 2003.

We laud the proactive steps being taken by Park managers to protect the

Park’s special cultural resources and to provide for the safety of visitors and

staff. However, we believe it is important to stress that fuel reduction in MVNP

is strictly a program of fire mitigation—it is not ecological restoration. To

remove >50 % of stand density and basal area from forests that have not been

subjected to any major disturbance for 700 years is to create a stand structure

that probably never existed previously—at least not within the last several centuries.

Because of the distinctive fire history of piñon-juniper forests in MVNP,

especially the paucity of past fire occurrences in this system, we recommend

against thinning and burning in areas remote from sensitive cultural sites. Not

only would such a program create stand structures and disturbance regimes

far outside the historical range of variability, but the treatments themselves

would create at least two new problems for managers to deal with. First, where

piñon-juniper forests in MVNP have burned or been cleared in the last century, they have not yet begun to recover their tree component. We have sampled

all of the large burns of the twentieth century (1934, 1959, 1972, 1989, and

1996), and have found almost no re-establishment of piñon or juniper even in

the oldest burns (unpublished data). The trees are thought to re-establish

only after shrubs have recovered adequately to provide sheltered micro-sites

for tree seedlings (Floyd 1982, Miller and Rose 1995, Wangler and Minnich

1996, Chambers 2001). However, we find no trees even in 50-year-old burns

in MVNP, where the shrubs are well established, suggesting that additional

mechanisms are involved in this area. We are initiating studies to elucidate the

reasons for this poor regeneration of trees after fire, but at present we do not

fully understand why piñon and juniper are so slow to re-establish after fire.

Considering that these are some of the oldest forests in the southwest, and

that they support a variety of old-growth fauna and flora (e.g., black-throated

gray warbler and Pedicularis centranthera), their conservation value should

be considered before subjecting them to what may be essentially irreversible

changes.

Our second big concern about extensive thinning and burning of Mesa

Verde’s piñon-juniper forests is related to what we may get in place of young

trees. Forests that burned in the 1930s through 1950s were replaced by

shrublands, through what appear to be normal successional processes. Indeed,

a major reason for the extensive shrublands at the northern end of MVNP is

the extensive fires that have occurred periodically during the last two centuries and possibly earlier (Erdman 1970, Floyd et al. 2000). However, the

piñon-juniper forests that have burned in the past two decades are being replaced by invasive non-native species, including muskthistle (Carduus nutans),

Canada thistle (Cirsium arvense), wild lettuce (Lactuca serriola), and the annual mustard Alyssum minor. Various post-fire mitigation actions (aerial seeding

of native grasses, mechanical removal, herbicides, and bio-control) have been

effective in reducing the density of weeds after fire, but none of these techniques has prevented the weeds from becoming major components of the

post-fire plant community (unpublished data). The burned areas also appear

vulnerable to invasion by cheatgrass (Bromus tectorum), which readily carries

surface fire. Cheatgrass invasion has caused drastic alteration of fire regimes in

much of the northern Great Basin (Miller and Tausch 2001). Cheatgrass expansion in Mesa Verde may be limited by the area’s cooler temperatures and

higher summer precipitation, compared with the northern Great Basin

(R. Tausch, personal communication). However, cheatgrass already dominates

some overgrazed rangelands at lower elevations near Mesa Verde, and it is

present, though not abundant, in the burned forests of MVNP. If it should

Romme, Floyd-Hanna, and Hanna Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs
USDA Forest Service Proceedings RMRS-P-29. 2003. 345

increase in the park, it could carry ever more frequent fires into the Park’s

ancient forests.

Thus, even though mechanical thinning is a very appropriate technique for

localized, small-scale protection of sensitive cultural resources, even in this

context it will require careful monitoring and weed control. And a more ambitious program of extensive thinning and burning of piñon-juniper forests in

MVNP could create an ecological disaster: weeds and more frequent fires could

potentially destroy nearly all of the ancient forests within a few decades. Such

a loss would be especially tragic if it resulted from well-intentioned but misguided efforts to “restore” the piñon-juniper ecosystem.

Implications for Piñon-Juniper Vegetation

Throughout the West

The piñon-juniper “type” covers a vast area in western North America, but

has received surprisingly little research attention given its ecological, economic,

and aesthetic importance. In particular, the natural range of variability in disturbance regimes and post-disturbance recovery processes is poorly understood.

We urgently need a more comprehensive understanding of piñon-juniper

dynamics so that we can direct our restoration and fire hazard mitigation efforts towards those stands and landscapes where such efforts are most

appropriate. As a first step, we suggest that it is useful to distinguish among

three fundamentally different kinds of piñon-juniper stand structures and fire

regimes, and to identify the environmental conditions with which each kind is

associated (table 1). We regard table 1 as a set of hypotheses, and hope that this

compilation will stimulate research to critically test these suggested relationships across the full geographic range of piñon-juniper vegetation in western

North America.

The first type in table 1 is what we call piñon-juniper grass savanna. Vegetation of this kind has been described in northern Mexico (Segura and Snook

1992), Arizona (Jameson 1962, Dwyer and Pieper 1967), and New Mexico

(Dick-Peddie 1993:87-93 (cited in Scurlock 1998: 206), C. Allen personal

communication), in places where soils are fine-textured, topography is gentle,

and summer moisture is relatively abundant. The well-developed grass component formerly carried frequent low-intensity fires that killed or thinned

encroaching trees and maintained an open woodland structure. Many of these

stands have been altered profoundly in the last century by grazing and fire

exclusion, which led to loss of the grass component, abnormal tree densities,

and abnormally severe fire behavior when the stands burn today. In such stands,

fire hazard mitigation via mechanical thinning and prescribed burning can be

linked to a broader goal of ecological restoration, and aggressive treatment of

this kind is an urgent need in many places.

The second type in table 1 is what we call piñon-juniper shrub woodland.

This kind of vegetation has been described in northern and central portions of

the Great Basin (Koniak 1985, Tausch and West 1988, Miller et al. 1995,

Miller and Tausch 2001) and probably also occurs in many portions of the

Colorado Plateau, where precipitation occurs mostly in winter, on deep soils

that support an abundant shrub layer (e.g., sagebrush). Shrublands generally

support more intense fires than grasslands, and fires in this vegetation type

probably always have tended to be stand-replacing. Prior to Euro-American

settlement in the mid-1800s, trees would become established during the firefree intervals that lasted from several years to a few decades, but nearly all of

Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs Romme, Floyd-Hanna, and Hanna
346 USDA Forest Service Proceedings RMRS-P-29. 2003.

Table 1—Structure and disturbance dynamics, distribution patterns, and current status of three contrasting types of piñonjuniper communities in western North America: a synthesis and set of hypotheses for further research. HRV = historic range

of variability.

Piñon-Juniper Grass Savanna Piñon-Juniper Shrub Woodland Piñon-Juniper Forest

Pre-1900 fire frequent, low-severity, surface moderately frequent, high-severity, very infrequent, very highregime fires ... carried by grasses crown fires ... carried by shrubs & severity, crown fires ... carried

trees by tree crowns

Pre-1900 sparse trees, few shrubs, sparse to moderately dense trees, dense trees, sparse to moderately

stand structure dense grass and other sparse to very dense shrubs, dense shrubs, sparse herbs

herbs moderately dense to sparse herbs

... all depending on time since

last fire

Pre-1900 stand low tree density and high seral trend from herb to shrub to tree stable/stationary tree age

dynamics herbaceous biomass dominance, interrupted periodically structure and little change in

maintained in part by recurrent by fire which returns a stand to early shrub or herbaceous layers

fire seral herb dominance during the long intervals without

fire ... very slow recovery after

fire

Post-1900 reduced fire frequency, great reduced fire frequency, small little change in fire frequency or

changes in increase in fire severity increase in fire severity fire severity

disturbance

regime

Post-1900 increasing tree density, increasing tree density, decreasing little change in tree density or in

changes in decreasing herbaceous biomass shrubs and herbs shrubs and herbs

structure

Overall current outside HRV for disturbance outside HRV for disturbance regime, still within HRV for disturbance

status regime, structure, & composition structure, & composition regime, structure, & composition

Implications for urgent need for active restoration urgent need for active restoration no need for restoration ... protect

restoration instead

Current stand very old trees (> 300 years) very old trees (> 300 years) absent very old trees (> 300 years)

age structure present, but not numerous or rare ... young trees (< 150 years) numerous ... stands with all-

... young trees (< 150 years) dominate stands aged structure, including old &

dominate stands young trees

Distribution: deep, fine-textured soils deep, fine-textured soils shallow, rocky, or coarse-textured

soil character- soils

istics

Distribution: summer peak in precipitation winter peak in precipitation variable

precipitation

regime

Distribution: gentle plains and broad valley gentle plains and broad valley rugged slopes, canyons, and

topographic bottoms, with few barriers to bottoms, with few barriers to fire mesa tops, with many barriers to

characteristics fire spread spread fire spread

Distribution: grasslands, ponderosa pine, or grasslands, big sagebrush, or other desert scrub, “slickrock,” or other

adjacent other types that burn frequently types that burn frequently types with sparse herbaceous

vegetation types vegetation that rarely burn

Geographic most common in northern Mexico, most common in the northern and scattered throughout the

distribution southern New Mexico & Arizona, central Great Basin, and the Colorado Plateau, Great Basin,

northern NM, and possibly SE Colorado Plateau central Oregon, southern Rocky

Colorado Mountains, and southern

California mountains

Examples Jameson 1962, Dwyer and Tausch et al. 1981, Koniak 1985, Tausch et al. 1981, Tress and

Pieper 1967, Segura and Snook Tausch and West 1988, Miller et Klopatek 1987, Kruse and Perry

1992, Dick-Peddie 1993, C. Allen al. 1995, Miller and Tausch 2001 1995, Wangler and Minnich

personal communication 1996, Miller et al. 1999, Tausch

and Nowak 1999, Floyd et al.

2000, Waichler et al. 2001

Romme, Floyd-Hanna, and Hanna Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs
USDA Forest Service Proceedings RMRS-P-29. 2003. 347

the trees would be killed by the next fire. Fire exclusion during the last century has allowed this normal successional process to proceed to the point of

tree dominance across large areas where trees were formerly sparse. Fuel loads

have become very high and continuous, and some recent fires probably have

been larger and more severe than would have occurred before the late 1800s

(Miller and Tausch 2001). As with the piñon-juniper-grass savanna, fire hazard mitigation via mechanical thinning and prescribed burning (some of it

designed to be stand-replacing) can be linked to a broader goal of ecological

restoration in the piñon-juniper shrub woodland, and aggressive treatment of

this kind is an urgent need in many places.

The third type in table 1 is what we call piñon-juniper forest. This kind of

vegetation has been described in scattered locations throughout the Colorado

Plateau (Tress and Klopatek 1987, Floyd et al. 2000), the Great Basin (Tausch

et al. 1981, Miller et al. 1999, Tausch and Nowak 1999), central Oregon

(Waichler et al. 2001), the mountains of southern California (Wangler and

Minnich 1996, Minnich and Everett 2001), and in central Arizona (Kruse

and Perry 1995). Rather than being associated with a particular soil type and

climatic regime, piñon-juniper forest appears to be restricted to an unusual

combination of soils and topographic conditions that protect the stands from

frequent fires. Soils are too shallow or too coarse-textured to support a continuous cover of grass or shrubs, such that fires tend to spread through a stand

only under conditions of extreme drought and wind. The topography is rugged and broken, with cliffs, bare slopes, or other natural barriers that tend to

prevent fires from spreading into a stand except under conditions of extreme

drought and wind. Thus, this kind of vegetation may escape fire for many

centuries and develop striking old-growth characteristics, including a dense,

multi-storied canopy with ancient living and dead trees. The old forests of

Mesa Verde have these characteristics, and Waichler et al. (2001) describe

western juniper forests in central Oregon that contain living trees >1000 years

old and dead trees nearly 2000 years old. When fire does occur in old piñonjuniper forest stands, however, it tends to be very severe and stand-replacing.

In dramatic contrast to the other two kinds of piñon-juniper vegetation (piñonjuniper grass savanna and piñon-juniper shrub woodland), most of the

piñon-juniper forest type probably has not been substantially altered by fire

exclusion in the last century, and probably is not outside its historic range of

variability in stand structure, fire frequency, and fire behavior. Thus, these

forests generally should not be subjected to extensive mechanical thinning or

prescribed burning, although fuel reduction may be appropriate in localized

areas to protect human lives, property, or other sensitive resources. Such

localized fire mitigation treatments should be called just that – they should

not be called “restoration” and they should acknowledge that relatively rare

and ecologically significant old-growth characteristics are being sacrificed to

protect other resources and values.

The piñon-juniper grass savanna (table 1) differs qualitatively from the other

two piñon-juniper types, in that its pre-1900 fire regime was characterized by

frequent, low-severity fires, whereas the piñon-juniper shrub woodland and

the piñon-juniper forest were dominated by infrequent, high-severity fires.

The piñon-juniper shrub woodland had shorter pre-1900 fire intervals than

the piñon-juniper forest (mean fire intervals perhaps <100 vs >100 years,

respectively), but both types probably followed similar successional trajectories after fire. However, the piñon-juniper forest type is distinct from the other

two types today, in that its natural fire regime and stand dynamics have been

disrupted to a far lesser extent by human activities of the past century.

Ancient Piñon-Juniper Forests of Mesa Verde and the West: A Cautionary Note for Forest Restoration Programs Romme, Floyd-Hanna, and Hanna
348 USDA Forest Service Proceedings RMRS-P-29. 2003.

We emphasize that all of the hypotheses in table 1 need further critical

testing. We also point out that, although this paper has focused on piñonjuniper vegetation, its conceptual framework probably is broadly applicable to

several other vegetation types. Despite the well-supported need for urgent

restoration in many areas (e.g., Covington et al. 1997), we believe that there

also are many ecosystems throughout the West in which Twentieth century

alterations of community structure and function have been minimal. Systems

that probably still remain within their historic range of variability include highelevation spruce-fir and lodgepole pine forests, which are naturally characterized

by infrequent but sometimes large, intense fires (Romme 1982, Veblen this

volume). Richard Minnich (personal communication) also suggests that the

fire regimes of numerous semi-desert and woodland vegetation types (including blackbrush scrub, Joshua tree woodland, and closed-cone conifer

woodlands) have been altered very little in the past century – at least in places

where non-native species invasions have not shortened fire intervals substantially. Even some ponderosa pine forests in northern Colorado and the Black

Hills may have been altered less by twentieth century fire exclusion than is

commonly believed (e.g., Shinneman and Baker 1997). These examples help

to underscore the primary message that we hope to convey in this paper: that

restoration treatments must be based on a good understanding of local stand

history and historic range of variability. Above all, we must avoid the temptation to apply “one-size-fits-all” prescriptions for management.

Acknowledgments

Our research in Mesa Verde has been supported by the National Park Service and by Prescott College. We thank the park staff, particularly Steve

Budd-Jack, Howard Dimont, Marilyn Colyer, Allan Loy, and Linda Towle,

for facilitating our research over the last 10 years. We also thank Albert Spencer, Marilyn Colyer, Craig Allen, and Brian Jacobs for insightful discussions

and observations of Mesa Verde’s ecology. We are grateful to Robin Tausch,

Rick Miller, Richard Minnich, William Baker, R. M. King, Dave Swift, and

Dan Manier for their critical reviews that helped to improve an earlier draft of

the manuscript.

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Landscape Planning
352 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 353

Expectation and Evaluation of Fuel

Management Objectives

Mark A. Finney1 and Jack D. Cohen1

Abstract—The success of fuel management in helping achieve wildland fire management goals is dependent first upon having realistic expectations. Second, the

benefits of fuel management can be realized only when treatments are applied at the

appropriate scale to the appropriate source of the problem(s). Scales range from the

site- or stand-level to landscape-level, but apply differently for purposes of benefiting wildland values than for increasing home survivability. Lastly, accomplishing the

broad goals for fuel management requires understanding how proposed treatments

directly contribute to solving specific problems. This process of finding solutions to

fire problems is framed in terms of “fire risk management” or reduction of “expected

loss.” This conceptually depicts the way that treatments can influence fire behavior

and thus produce benefits by reducing losses and it avoids the unrealistic expectations that fuel management will stop wildfires and prevent homes from burning.

Introduction

Fuel management is receiving increasing attention as a means of modifying

wildland fire behavior and mitigating threats to the urban interface (National Fire Plan http://www.fireplan.gov/), including the Cohesive

Strategy (http://www.fireplan.gov/cohesive_strategy_1_28_02.cfm) and 10-year

Comprehensive Plan (http://www.fireplan.gov 10_yr_strat_pg_1.html). The

rationale for treating fuels follows from:

1) recent and well publicized failures of fire suppression to protect

wildlands and developed areas under extreme fire conditions

(Colorado/Arizona/Oregon 2002, Montana/Idaho/Colorado 2000,

California 1987), and

2) the realization that the extreme nature of these fires has sometimes

been exacerbated by human modification of fuel conditions.

Large fires burning under extreme conditions of high winds and low humidity are difficult, if not impossible, to suppress. These extreme weather

conditions are expected regularly during the fire seasons of the western United

States. The prevalence of extreme fire behavior in low-elevation forests is,

however, partly a consequence of effective fire suppression during the past

century. Exclusion of historically frequent fire from these ecosystems has resulted in dramatic changes to vegetation structure and fuels compared to

conditions in the 19th century (Wilson and Dell 1971; Arno and Brown 1989,

1991). These alterations of the fuel structure, specifically the in-growth of

trees and accumulation of dead woody fuels, tend to readily support extreme

fire behavior (crown fire, spotting). This reduces the effectiveness of fire suppression and creates uncharacteristically severe effects in those ecosystems

compared to pre-existing ecological disturbance regimes. Management of these

fuels directly is, therefore, seen as a proactive means to change fire behavior

1USDA Forest Service Fire Sciences

Laboratory, Rocky Mountain Research

Station, Missoula, MT.
354 USDA Forest Service Proceedings RMRS-P-29. 2003.

and effects (Brackebusch 1973; Davis and Cooper 1963; Kallender 1969;

Koehler 1992; Martin et al. 1989; Wood 1982). The need for fuel management solutions has recently been made especially acute in these low-elevation

areas because of human encroachment and development of areas formerly

classified as wildlands.

Although the conceptual basis of fuel management is well supported by

ecological and fire behavior research in some vegetation types, the promise of

fuel management has lately become loaded with the expectation of a diffuse

array of benefits. Presumed benefits range from restoring forest structure and

function, bringing fire behavior closer to ecological precedents, reducing suppression costs and acres burned, and preventing losses of ecological and urban

values. For any of these benefits to be realized from fuel management, a supporting analysis must be developed to physically relate cause and effect,

essentially evaluating how the benefit is physically derived from the management action (i.e. fuel management). Without such an analysis, the results of

fuel management can fail to yield the expected return, potentially leading to

recriminations and abandonment of a legitimate and generally useful approach

to wildland fire management.

In this paper, we seek to improve the performance and acceptance of fuel

management by examining:

1) common expectations of fuel management effects and performance

compared to reality,

2) some implications of recent research on fuel treatment scale,

prescriptions, and locations, and

3) goals, objectives, and using the concept of “fire risk” to support and

direct fuel treatment projects.

Expectations for Fuel Management

A number of false or exaggerated expectations are endemic to the general

public and fire management organizations alike. The persistence of these expectations serves to hinder the proper use of fuel management because:

1) they suggest excessively high standards for the success of the fuel

treatment,

2) the scope of application or benefits is too broad for a single fuel

treatment technique, and

3) they transfer responsibility for fire losses or fire protection to the wrong

people or place.

Some of these perceptions or expectations are listed in table 1 along with

clarifications as to more realistic views.

Local and Lanscape Scales of Fuel Management

The process of developing specific objectives for fuel treatments and evaluating how treatments might perform necessarily requires an explicit

consideration of spatial scale. Two basic scales can be identified with respect

to the way that fuel management affects fire behavior. The local scale applies

to fuel management efforts within a forest stand, a treatment unit, and next to

and including a house or structure. Surface fuels removed by prescribed burning or canopy fuels removed by thinning change fire behavior within the local

domain of the treatment unit. Many studies have shown that fire behavior

Finney and Cohen Expectation and Evaluation of Fuel Management Objectives
USDA Forest Service Proceedings RMRS-P-29. 2003. 355

responds at this local scale to fuel management measures (Helms 1982;

Martin et al. 1989; Deeming 1988; Pollet and Omi 2002). This scale, and

only this scale, corresponds to the physics of home ignition, whether from firebrands or flames impinging upon home construction materials (Cohen 2000b).

The physical properties of the home and its immediate surroundings determine

ignition potential and are restricted to the structure and material in very close

proximity as determined by principles of radiation and convective heat transfer.

The other scale is described here as the landscape scale when concerning

wildland areas, or the community scale with respect to urbanized environments.

This broad scale is a collection of elements from the local scale. That is, wildland landscapes are composed of many stands and treatment units whereas

communities are composed of various combinations of structures and undeveloped lots. Many wildland fires are almost an archetypal landscape process

because they are larger than a single stand or structure and they move over,

across, and through the collection of smaller scale elements like forest stands

and homes. Thus, the fire behavior at these broader scales involves the topology or spatial arrangement of stands and homes, each affecting the fire at its

own local scale. This spatial arrangement of stands and homes is crucial to

determining the success of fuel management activities in changing effects of

Table 1—Expectations of fire and fuel management compared to more realistic performance.

Expectation Reality

Adding more firefighting The reality is that fire suppression works except when it doesn’t. Most fires are

resources will reduce already successfully attacked (~96%) leaving the rest to burn under conditions

acres burned too extreme for suppression success. More firefighting resources can be

expected to change wildfire acreage very little because only a slim fraction

(~4%) of fires currently escape. Furthermore, it makes little sense to increase

fire suppression efforts to solve a fire behavior problem that is widely

recognized as having been exacerbated by fire suppression effects on fuels.

Structures and homes Urban interface fires typically overwhelm resources because of the extreme

will be protected by conditions under which they occur (i.e., when fire suppression fails). Thus,

firefighting resources exposure of dozens of structures simultaneously to fire brands and fire

encroachment exceeds the capacity of existing suppression forces to protect

and extinguish them. The problem is compounded in dense neighborhoods

when structures start to burn or become fully involved because of their

tendency to ignite adjacent structures.

Wildland fuel management Wildland fuel management changes wildland fire behavior. Structure loss (i.e.,

prevents structure loss homes burning) is dependent on local properties of the structure and its

immediate surroundings. This means that the proximate responsibility for

structure loss from fire primarily resides with the private owners of the structure

and immediate property, not with public land management agencies.

Fuel treatments will stop Fuel treatments change fire behavior within limitations of their prescription. That

wildland fires is, the design criteria or prescription of fuel treatments (see below) allows them

to perform alterations in fire behavior up to a limit of weather conditions

(primarily fuel moisture and winds). This change in behavior includes reduced

intensities and spread rates, but does not prevent combustion. The changes in

fire behavior and fuel conditions may enhance the effectiveness of fire

suppression tactics, but it is impossible for fuel treatments alone to stop fires

from burning or spreading.

Fuel management can be Fuel management can alter fire behavior but the longevity of these alterations and

equally successful for all the ecological appropriateness of the treatment are specific to a given

vegetation and fire regimes. vegetation type. The most common fuel treatments today are concomitant with

forest restoration of low-elevation pine and mixed-conifer forests. The same

ecological justification and desired changes in fire behavior are inappropriate

models for fuel hazard reduction in grasslands that recover following a single

growing season or to high-elevation forest characterized by stand-destroying

fire regimes. Fuel management strategies and ecological rationale are required

for each fire regime and vegetation community.

Expectation and Evaluation of Fuel Management Objectives Finney and Cohen
356 USDA Forest Service Proceedings RMRS-P-29. 2003.

large fires either at the local or landscape scale. Individual treatment units,

regardless of their shape or position, will be irrelevant to the progress and

behavior of the fire at the landscape scale unless the spatial nature (topology)

of treatment arrangement is considered.

Stand Level Prescriptions for Fuel Management

Fire behavior responds to fuels, weather, and topography. Changes to fuels,

for example from prescribed burning or thinning, are related to potential fire

behavior at that site and have resulted in reduced severity of wildfires where

fuel treatments have occurred (Martin et al. 1989; Helms 1979; Agee 1998).

For many fuel management objectives, the goal is to limit surface fires from

becoming crown fires. To design a fuel management prescription within a

treatment unit, prescription elements must specify changes to specific fuel

attributes. These fuel attributes must be connected to a desired change in fire

behavior through some physical mechanism. Such a physical mechanism relating surface and crown fires was described by Van Wagner (1977, 1993). His

formulation identifies two thresholds that define crown fire activity. Crowns

are ignited after the surface fire reaches a critical fireline intensity relative to

the height of the base of the aerial fuels in the crown. This crown ignition can

become an “active” crown fire that spreads much more rapidly through the

crowns, if its spread rate is high enough to surpass the second threshold based

on the crown bulk density (kg m-3). Thus, Van Wagner’s (1977) relationships

suggest that fuel management prescriptions can limit crown fire activity by

first reducing surface fuels to limit fireline intensity, then thinning the smallest

trees or pruning to elevate the base of aerial fuels from the ground surface. A

final measure may involve crown thinning (removal of some canopy level trees)

to make difficult the transition to active crowning. This linkage between surface and crown fire has been described by Scott and Reinhardt (2001) and

provides a method for determining stand-level prescriptions for fuel management.

Landscape Level Treatment Planning

Fire and fuel managers are familiar and generally comfortable with developing prescriptions for individual stands, whether for silvicultural purposes, forest

restoration, or wildland fuels treatment as described above. However, an individual stand treated to a given prescription will probably be irrelevant to fire

behavior and effects at the landscape scale because wildfires are often larger

than individual treatment units (Salazar and Gonzalez-Caban 1987; Dunn

1989). Thus, some means of spatially organizing treatment units must be

considered in order to accomplish the landscape level goals for fuel management. Brackebusch (1973) suggested large-scale frequent mosaic burning.

Another landscape strategy described by Finney (2001) seeks specifically to

disrupt fire growth and modify fire behavior rather than to stop fires since the

latter is not realistic (see Expectations above). Strategic area treatments (Finney

2001; Hirsch et al. 2001) create landscape fuel patterns that slow fire growth

and modify behavior while minimizing the amount of treated area required

(figure 1). Similar ideas in forest management have been developed to achieve

spatial harvest objectives (Baskent and Jordan 1996; Baskent 1999). The impetus follows from limitations on the amount and placement of fuel treatments

because of land ownership, endangered species, riparian buffers, etc. It has

precedence in the way that natural fire patterns serve to fragment fuels across

landscapes to produce self-limiting fire growth and behavior as shown in

Yosemite National Park (van Wagtendonk 1995), Sequoia National Park

Finney and Cohen Expectation and Evaluation of Fuel Management Objectives
USDA Forest Service Proceedings RMRS-P-29. 2003. 357

(Parson and van Wagtendonk 1996), and Baja California (Minnich and Chou

1997). Landscape analysis of fire behavior and spread patterns is now prompting research into computer software for optimally locating fuel treatments for

slowing fire growth and limiting effects (figure 2).

A frequently proposed alternative to this strategic landscape approach involves the fuel break concept (Weatherspoon and Skinner 1996; Agee et al.

2000). The stated purpose of fuel breaks is to reinforce an existing defensible

location for use by fire suppression forces in stopping fire spread (Green 1977).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

0.2

Fraction of Landscape Treated

Relative Spread Rate

Random

Partial Overlap

Complete Overlap

(parallel strips)

Figure 1—Overall fire spread rate as

a function of treatment fraction for

different spatial patterns of treatment

units (from Finney 2001 and Finney

2003, treatment assumed to reduce

spread rate to 0.2 of the untreated

fuels). Compared to patterns that

require overlap among treatments,

the random treatment pattern

produces little reduction in overall

fire spread rate until relatively large

proportions of the landscape are

treated (because fire goes around the

treated patches).

Figure 2—Routes resulting in the

most burned area can be identified

using graph theory (Finney 2002).

These routes reflect the greatest

opportunities for disrupting the

simulated fire growth using fuel

management. Red indicates high

influence and blue little or none.

Heuristic algorithms can then

optimize fuel treatment locations

(shown in black) that result in

efficient reductions in fire spread rate

per unit area treated. The treatments

shown in black reduce fire spread

rate by 40% with less than 16% of

the area treated because treatments

are located to block the fastest and

most influential routes.

The putative benefits of fuel breaks are achieved when undesirable fire effects

are avoided by holding fires to smaller sizes. No change in behavior or effects

is achieved away from the fuel break or if fuel breaks fail to stop fires. Thus,

fuel break performance and benefit is based on the questionable expectation

that fire suppression will be capable of “stopping” fires after initial attack fails

(see Expectations above). Large fires escape initial attack for many reasons

Expectation and Evaluation of Fuel Management Objectives Finney and Cohen
358 USDA Forest Service Proceedings RMRS-P-29. 2003.

that include resource scarcity due to high numbers of ignitions, and spotting

and crown fire behaviors that make holding a pre-defined position by firefighters

untenable and perhaps dangerous. Furthermore, the only firefighting tactics

supported by fuel breaks are categorized as “indirect” (Brown and Davis 1973).

This means that the rest of firefighting tactics (direct attack and parallel attack) are not enhanced regardless of the current fire behavior or fire position

on the landscape relative to the location of the fuel break. A large fire that

slows before reaching a fuel break (because of a change in weather conditions,

nighttime, etc.) must be attacked (by direct or parallel tactics) with no benefit

of the fuel break. Utilizing fuel breaks involves a large burnout operation,

which may be of a size equal to the original wildfire, take place regardless of

the fire behavior at its current location, and produce negative effects on wildland vegetation greater than the original wildfire. Maintenance costs of fuel

breaks are often ignored by proponents but maintenance is a perpetual burden that is likely to divert efforts from managing fuels and vegetation on the

remaining majority of the landscape.

Structure Ignition

Research findings indicate that a home’s characteristics and the characteristics of a home’s immediate surroundings within 30 meters principally determine

the potential for wildland-urban fire destruction. This area, which includes

the home and its immediate surroundings, is termed the home ignition zone.

The home ignition zone implies that activities to reduce the potential for wildland-urban fire destruction can address the necessary factors that determine

ignitions and can be done sufficiently to reduce the likelihood of ignition. Wildland fuel reduction outside and adjacent to a home ignition zone might reduce the potential flame and firebrand exposure to the home ignition zone

(i.e., within 30 m of the home). However, the factors contributing to home

ignition within this zone have not been mitigated. Given a wildfire, wildland

fuel management alone (i.e., outside the home ignition zone) is not sufficient

nor does it substitute for mitigations within the home ignition zone.

The home ignition zone applies to a single home as well as a neighborhood

of homes in proximity. The fire physics and the requirements for ignition do

not change with increasing housing density, but the social response changes.

Homes in areas where the home ignition zone falls within property boundaries can largely be addressed as individual homes without interaction with

other homes. As densities increase and home ignition zones extend across

property lines, the ignition potential of the home ignition zones depend on

the activities of more than one property owner. At higher housing densities

more than one house can fall within a home ignition zone. In such cases a

neighboring house burning may become a flame and firebrand source for igniting adjoining homes. When homes share home ignition zones, the

wildland-urban fire problem must become a collective, community effort. Wildland-urban fires such as Panorama (San Bernardino, CA 1980), Baldwin

Hills (Los Angeles, CA 1985), Oakland (Oakland, CA 1991), and Laguna

(Laguna, CA 1993) indicate that communities that do not act collectively to reduce their home ignitability have a high potential to burn collectively.

These findings were based on a diverse research approach utilizing modeling, experiments, case studies, and wildland-urban fire investigations. The

model calculations were made on the assumptions of intense fire conditions

(e.g., crown fire) and ideal heating characteristics (flames radiating as an ideal

radiator, a black body). Model estimates of direct flame heating indicated that

wood ignition would not occur during the burning duration of crown fire

flames at distances greater than 30 meters (Cohen 2000a). Experiments were

Finney and Cohen Expectation and Evaluation of Fuel Management Objectives
USDA Forest Service Proceedings RMRS-P-29. 2003. 359

conducted to check the results of the modeling. The experimental crown fires

provided radiation and convection heating as well as firebrands capable of

numerous spot fire ignitions. Home ignition studies during the International

Crown Fire Modeling Experiment (Alexander and others 1998) showed that

wood walls only ignited at distances from the crown fires closer than 20 meters.

Wood walls at 20 and 30 meters did not ignite or significantly scorch. The

home ignition experiments indicated that model calculations over-estimated

the distance at which a wood wall would ignite (Cohen 2000a). Two former

case studies analyzed home survival during severe wildland-urban fires. The

case studies found that 85 to 95 percent home survival largely depended on

two factors—a nonflammable roof and vegetation cleared within 10 meters of

a home (Howard and others 1973; Foote 1994). Investigations of severe

wildland-urban fires indicated that home destruction was not necessarily caused

by the nearby flames of the intense crown fires; less intense surface fires spreading to the home or direct ignition from firebrands ignited the homes.

Investigations also revealed severe wildland-urban fire destruction associated

with nearby low intensity surface fires (Cohen 2000b) as well as surviving

homes (without protection) surrounded by intense crown fires (Cohen in process). The possible associations between wildland fire behavior and home

survival can be displayed in an association matrix (figure 3). Because homes

survive high intensity fires and are destroyed in low intensity fires (Cohen

1995; Cohen and Butler 1998; Cohen 2000a; Cohen 2000b; Cohen 2001) it

is questionable whether wildland fuel reduction activities are necessary and

sufficient for mitigating structure loss in wildland urban fires.

yes

yes

no

no

y,y y,n

n,y n,n

expected unexpected

unexpected expected

HOME SURVIVAL

LOW

WILDLAND

FIRE

INTENSITY

Figure 3—An association matrix

depicts the assumed relationship

between wildland fire intensity and

home survival. The frequency with

which combinations (y,n) and (n,y)

have been observed supports the

results of physical modeling in

questioning the dependency of

home survival on wildland fire

behavior and fuel management

conducted in wildlands.

Goals and Objectives of Fuel Management

The purposes of national fuel management activities in the United States

are described by the broad goals stated in the National Fire Plan. These policy

documents identify general goals for fuel management activities as:

1. Reduce risk of catastrophic fire

2. Protect communities

3. Reduce fuel hazards

4. Reduce wildfire acres and costs

5. Restore fire-adapted ecosystems

While adequate to express general directions for national fuel management

policy, these statements are not intended to provide specific guidance to fieldlevel fuel treatment projects. The “field-level,” for this paper, describes the

organizational level (e.g., USFS district) where specific fuel treatment units

Expectation and Evaluation of Fuel Management Objectives Finney and Cohen
360 USDA Forest Service Proceedings RMRS-P-29. 2003.

are identified and landscape planning is performed. This is the critical level

that determines the success or failure of fuel management where the “rubber

meets the road” and the fire meets the fuels. In other words, the success of an

entire national policy hinges on the success of fuel treatments accomplishing

the field-level benefits promised and expected.

If the broad policy goals are to be used to guide field-level projects, a set of

specific objectives must be developed to justify field-level fuel treatment plans.

These objectives must be based on a local problem analysis and have standards

for evaluating the success or failure of the project. The following are steps that

can help bring these broad policy-oriented goals down to specific objectives

that permit treatments to be designed, evaluated, and justified:

1. Identification of the specific problems to be addressed by fire/fuel

management.

2. Identification of the cause of these problems as relating to fuels or fire

behavior.

3. Description of the desired outcome of the treatment measure (i.e.,

how much change is needed).

4. Identification of the appropriate scale of treatment needed to effectuate

the desired outcome.

5. Description of the specific cause and effect relationship between the

desired outcome and the proposed treatment(s).

Despite the apparent differences among the general policy goals, the fieldlevel problems associated with them are almost identical. All deal with fuels

and the dynamics of the local ecosystems, potential fire behavior, and the

likelihood of undesirable effects of fire on urban and wildland areas (costs,

losses, expenses). This suggests that the broad policy goals are so closely linked

that a unified means of describing this linkage would facilitate understanding

how to accomplish all of them. We suggest that all of these goals can be collapsed into the single broad category of “fire risk management.” As the term

suggests, fire risk is managed, not eliminated. That is, we don’t eliminate natural

disturbances, we mitigate the associated human disasters.

“Fire Risk Management” and Expected Loss

Risk is a word commonly used to describe threats from fire but it suffers

from ambiguous meaning. The absence of a consistent and precise definition

of “risk” hinders communication and, more importantly, the possibility of

actually achieving a reduction in fire “risk” through fuel management. In other

words, “You can’t do what you can’t say.” Historically, risk was used for fire

prevention and was equated to the probability of a fire starting (Brown and

Davis 1973). These data could be obtained from historical records for a local

jurisdiction and partitioned according to location and cause. Although relatively easy to measure, this component has little to do with more critical

questions concerning whether the fire once started would achieve a given size,

burn a particular area, or cause a particular effect. The probability of burning

and consequence of burning are far more relevant to the business of fuel and

fire management than the probability of fire starts, but are completely different with respect to the method of calculating or estimating them.

Outside the realm of wildland fire management, risk is often expressed in

terms of expected loss. Insurance companies calculate the expected loss of a

home ($/year) so that the owner’s premium can be determined. Despite the

vagueness of its colloquial usage, “risk” defined as an expected loss has an

exact mathematical formula involving the product of two numbers: 1) the

probability of the event, and 2) the value change in the property because of

Finney and Cohen Expectation and Evaluation of Fuel Management Objectives
USDA Forest Service Proceedings RMRS-P-29. 2003. 361

the event. Wildland fires don’t necessarily result in loss or negative consequences, so a more appropriate term would be expected net value change. Wildland fires have many different behaviors (e.g., intensity, spotting) that

can produce value changes (e.g., fuel reduction, tree mortality, sedimentation

of watersheds, structure damage). Since fire behaviors vary in place and time,

there would be a distribution of behaviors and a distribution of corresponding

changes in value (benefits and losses). In other words a theoretical expected

net value change (Envc) from a wildland fire at a given location on the ground

could be obtained for all N categories of a given fire behavior:

( )[ ] i i

Ni

E nvc=

Â

pFiB-L

=1

where F

i is a given fire behavior (e.g., fireline intensity, firebrand density) and

B

i and 

L

i are the respective benefits and losses

resulting from that fire behavior

(e.g., dollars from structure loss or tree mortality). Benefits and losses can be

combined into a single net value change, but separating the terms in this

equation emphasizes the importance accounting for potential benefits of some

wildland fire behaviors to some wildland values in addition to losses. Note

that p(F

i

)is the probability of the i

th category of the fire behavior occurring

and B

i and Li are the respective benefits and losses for the 

i

th fire behavior

category. This kind of equation would apply separately to each value of concern and related fire behavior(s).

To apply the expected value change to wildland fires, research is required to

find ways to estimate the parts on the right hand side of the equation. The

first part p(F

i

) (probability distribution of fire behavior) is particularly challenging because wildland fires are spatial and dynamic, occurring at different

places and times and burning over space and time. A brute-force approach to

calculating wildland fire probabilities would entail estimating fire growth across

the landscape from every ignition point on a landscape, for every ignition date

and sequence of weather, for all possible fire seasons and suppression responses.

A given cell or node on the landscape would burn differently by backing,

flanking, and heading fires depending on the relative location of the ignition

and ensuing spread. Each cell or node on the landscape would thus have a

probability distribution of fire behaviors represented by p(F

i

) in the equation

above. The second parts B

i and Li of 

Envc would then need to be determined

for each fire behavior in the distribution (e.g., dollars lost for a specified level

of fireline intensity). Some fire behaviors cause benefits to some values (e.g.,

fuel reduction, ecosystem health) but others can result in a total loss. That is

the reason that fire expected loss would be the product of two probability

distributions, one for the fire behavior and one for the net change in value

(benefits minus losses) resulting from that fire behavior. If Envc could be calculated for all values, then their sum at a given site and over an entire landscape

would provide maps that spatially ordinate “risk.” These would be spatially

sensitive to all scales of fuel management, from the local properties of structures to landscape-level fuel treatments. If ecological modeling and forecasts

can be made of future landscape conditions, then cumulative Envc up to a given

future date can be estimated, permitting tradeoffs and opportunity costs to be

compared for different action plans. Only then will it be possible to examine

the long-term ramifications of today’s action or inaction, which very likely

will be different from the short-term effects.

Although complicated and difficult to calculate, this mathematical definition of fire risk as an expected value change clearly demonstrates the ways that

wildland and urban fuel management activities influence the components of

fire risk. Fuel management in wildlands changes the probability that wildland

Expectation and Evaluation of Fuel Management Objectives Finney and Cohen
362 USDA Forest Service Proceedings RMRS-P-29. 2003.

fires move across the landscape, and whether they ultimately impinge on urban areas containing structures, or result in fires of different sizes and ecological

effects. Thus, wildland fuel management changes the first part of the equation

in terms of probability of a fire reaching a given location. It also changes the

distribution of fire behaviors and ecological effects experienced at each location because of the way fuel treatments alter local and spatial fire behaviors

(Finney 2001). The probability that a structure burns, however, has been

shown to depend exclusively on the properties of the structure and its immediate surroundings (Cohen 2000a). This means that construction materials

and their condition at the time of fire exposure that abate ignition from firebrands or flames change the second part of the risk equation only (e.g., replacing

wood shingle roof with asphalt shingles changes the structure response to fire

behavior). Changes to the flammable materials immediately surrounding the

house affects the fire behavior distribution in the first part of the risk equation. Thus, fire risk as Envc can be improved for wildland and urban areas by:

1. Changing wildland fuels for a “fireshed” involving a wide area around

the community (for many miles that include areas that fires can come

from). This changes probability of fire movement and skews the fire

behavior distribution by increasing the relative frequency of milder

behaviors.

2. Treating fuels and reducing fire behavior immediately adjacent to the

structures. This changes the fire behavior relevant locally to the ignition

of structures.

3. Changing the properties of the structure. This improves its response

when exposed to a given fire behavior.

The formulation of Envc also implies that risk is completely eliminated (goes

to zero) when values vanish (total value and value change). This means that

human systems of valuation are really at the heart of the idea of risk. There

would be no expected loss if humans didn’t exist, humans placed no values on

wildlands or developed property, or the values didn’t change as consequence

of fire. More importantly, this shows that both the causes of risk and the

solutions to risk reduction lie with human beings, not wildlands or natural

dynamics. That is, a change of human perspective can make problems appear

or disappear without changes in biophysical reality. The importance of human

values in Envc also suggest the possibility that the necessarily long-term (multidecade) management solutions based on Envc (if it could even be calculated)

could be outdated by changing social and political values during those time

periods.

The concept of expected net value change in managing fire risk encompasses all of the broad policy goals detailed above. Community protection as a

goal expresses the desired reduction in expected losses and maximizing

expected benefits (maximize Envc). Reducing fuel hazards involves local- and

landscape-scale fuel modifications that limit fire behavior and thereby diminish the losses (p(Fi

)* L

i

) and increased ecological benefits (p(F

i

)* B

i

). Reducing

fire suppression costs and wildfire acres is produced by changes in the probability distribution of fire sizes brought about by landscape fuel management

and reduced duration of extended attack and difficulty with suppressing smaller

and less extreme fires. Ecological restoration may also be addressed as diminished fire behavior, reduced losses of ecological values, and increased

sustainability of ecosystems that are properly managed. Lastly, it is likely that

quantitative assessment of Envc would lead to more realistic perspectives and

expectations for the effects of fire and fuel management activities because

alternative management scenarios could be compared and interpreted based

on a common methodology.

Finney and Cohen Expectation and Evaluation of Fuel Management Objectives
USDA Forest Service Proceedings RMRS-P-29. 2003. 363

Community Protection

As an example of how the components of risk management apply to the

above policy goals, we can look at the term “community protection.” The

term community protection is one of the most widely and prominently stated

goals for fire and fuel management. A community is really a collection of

many tangible and intangible parts that are held in common, including both

developed areas and wildland areas:

1. Structures, neighborhoods, businesses

2. Infrastructure (roads, bridges, rivers, dams, airports)

3. Lifestyle and economy (recreation, agriculture, extractive industries

like logging, mining)

4. Environment (scenery, air quality, wildlife, natural hazards like fire,

earthquakes, hurricanes)

Such diverse community values makes it difficult to justify any single overt

fuel management tactic on the basis of “protecting” all aspects of a community from wildland fires. Protection afforded to one component by a given

tactic (for example, localized fuel management for structure protection) may

little benefit other values (like scenery, air and water quality, or recreation

opportunities). The expected loss concept suggests that treatment tactics must

be partitioned according to their specific fire behavior changes that are appropriate and relevant to the response of the values concerned. That means

essentially treating wildlands separately from developed areas because the

effects and scales of those effects are not uniformly applicable.

To benefit the urban portions of a community, fuel management research

suggests that fuel management activities need only be concerned with the

fuels in the immediate proximity of the structures – within their ignition zone.

The material properties of the structures themselves are also important, and

managing fuels within the home ignition zone is shown to be the most effective at reducing the nearby sources of firebrands and combustible fuels and

vegetation that are commonly associated with structure ignition. When fires

occur, structures are less likely to be “lost,” thus reducing the expected net

value change of the urban values.

Wildland fuel management in low-elevation forest types, extending perhaps

many kilometers away from urban locations, however, is critical to reducing

the likelihood that wildland fires will spread to urbanized areas and pose ignition threats. Wildland fuel treatments can reduce the probability portion of

the expected net value calculation by changing fire behaviors at long distances

as well as fire movement. These changes in fire behavior increase the effectiveness of fire suppression, especially during initial attack by slowing fire growth

and limiting spotting. They also increase the survivability and resilience of

low-elevation forest vegetation to the inevitable wildland fire, thereby benefiting the wildland values of the community. Because urban fire disasters often

result from wildland fires igniting tens of kilometers away from urbanized

areas under extreme weather conditions (e.g., the Hayman fire in Colorado

and the Rodeo-Chedeski fires in Arizona in June 2002), wildland fuel management activities must be located broadly across those landscapes. Evidence

that fuel breaks surrounding urban zones are sufficient to reduce threats to

urban values is lacking. Because of their location on the periphery of wildlands, fuel breaks cannot reduce losses of wildland values associated with a

community. Although it is commonly argued that fuel breaks will reduce wildfire

intensities adjacent to residential development and thereby allows firefighters

to protect homes, wildland-urban fire disasters tend to occur during severe

fire conditions when fire behavior characteristics often overwhelm fire protection

Expectation and Evaluation of Fuel Management Objectives Finney and Cohen
364 USDA Forest Service Proceedings RMRS-P-29. 2003.

resources. These fuel treatments may facilitate firefighter effectiveness but only

if firefighters are available to be effective. Given homes with high ignition

potential and without fire protection, even a low intensity wildfire can result

in severe wildland-urban fire destruction as exemplified by the 2000 Los Alamos

fire destruction (Cohen 2000b).

In the context of fire risk management, the general goal of “community

protection” can only be accomplished if treatments satisfy the principles of

being necessary and sufficient for the specific elements of the risk equation.

Wildland fuel management is necessary to change wildland fire behavior (p(Fi

)),

but to be effective at mitigating risk for landscape-level community values and

adjacent developments it must be accomplished in sufficient amounts and patterns. Wildland fuel management is, however, not sufficient alone to abate

threats to home ignition. Susceptibility of homes to damage involves different

factors than wildland resources (e.g., construction standards vs. tree species)

and relates to different fire behaviors than do wildland resources (e.g., firebrands vs. fireline intensity). To reduce expected loss from home ignition, it is

necessary and often sufficient to manage fuels only within the home ignition

zone (change p(Fi

)) and abide by fire resistant home construction standards

(change Li

).

Conclusions

We suggest that problems to society posed by wildland fires are analogous

to those of traffic accidents. Traffic accidents cannot be stopped, either by

increasing the police force, or by reducing speed limits. No government agency

or politician believes it possible to stop them altogether. The consequences of

traffic accidents that do occur, however, can be mitigated by engineering safety

features into automobiles (airbags, seatbelts, frame design) as well as transportation infrastructure (modifying bridge abutments, steep curves, etc.). Likewise,

wildland fires cannot be stopped, either with an increasing firefighting budget

or fire prevention efforts. Wildland fires will always occur, and ecologically, we

know that they must occur in many ecosystems; excluding them is not desirable even if it was possible. The challenge for fire management is to reorient

the focus of efforts toward limiting the undesirable effects of fires on ecosystems and human development, not stopping fires. Similar to traffic safety

engineering, this paper describes approaches to engineering wildland landscapes and home ignition zones that make our societies more compatible with

wildland fires. Sustainability of wildland ecosystems can be accomplished by

managing fuels and landscape pattern to change fire behavior. Structure survival can be greatly increased by separate efforts that adopt readily available

construction standards and maintain fuel conditions in the home’s immediate

vicinity. Expectations of our society must also become aligned with the reality

of coexistence with fire and its positive and negative effects.

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Finney and Cohen Expectation and Evaluation of Fuel Management Objectives
USDA Forest Service Proceedings RMRS-P-29. 2003. 367

Scheduling Removals for Fuels Management

John Hof1 and Philip Omi2

Abstract—We explore management science options for scheduling the placement

of fuels reductions. First, we look at approaches for creating and maintaining a prespecified set of forest conditions that are deemed desirable from a fuels management

perspective. This approach is difficult because the pre-specified forest is hard to

determine and because it is an any-aged forest management problem that is intrinsically nonlinear. Second, we look at capturing the spatial relationships suggested by

fire behavior in long-term fuels management. This approach is difficult because fire

origins and behavior can be quite random and unpredictable. It is necessary to accept a particular fire event (analogous to the 500-year flood in flood-control planning)

as the target for fuels management. Overall, we conclude that long-term fuels management presents a formidable problem for management scientists. Numerically

intense stochastic programming methods, such as Monte Carlo repetitions with a fire

simulator, may be the most promising approach.

Background

The use of mathematical models for managing fires has a rich and varied

history in North America. Martell (1982) traces the application of operations research methods in forest fire studies back to the early 1960s. The

earliest potential applications of operations research techniques to wildland

fire management were mentioned by Shephard and Jewell (1961). Follow-up

work by Parks and Jewell (1962) generated considerable interest by examining the use of differential equations and calculus to identify the optimal

suppression force for a forest fire. Swersey (1963) and McMasters (1966)

extended Parks and Jewell’s work by focusing on the optimal mix of different

suppression units and the effects of labor constraints on resource allocation rules.

Growing familiarity with optimization techniques spawned additional fire

management applications, notably analyses of detection options (Kourtz and

O’Regan 1971) and airtanker retardant delivery systems (Simard 1979,

Greulich and O’Regan 1982). Fire suppression has continued to receive considerable interest through the use of optimal control theory (Parlar and Vickson

1982), nonlinear programming (Aneja and Parlar 1984), and catastrophe theory

(Hesseln et al. 1998). In addition to optimization, simulation modeling has

provided useful insights for evaluating management alternatives, especially in

an uncertain decision environment (Ramachandran 1988, Fried and Gilless 1988,

Mees and Strauss 1992, Mees et al. 1993, Gilless and Fried 1999). Other simulation work oriented towards allocating management resources in fire containment

efforts includes Mees (1985), Anderson (1989), and Fried and Fried (1996).

Boychuk and Martell (1988) used Markov chains to analyze seasonal hiring

requirements. Martell et al. (1989) modeled seasonal variation in the occurrence of human-caused forest fires. Mills and Bratten (1982) described the

use of an economic efficiency system for minimizing the “cost plus net value

change” of various fire management alternatives. And, a number of expert

1Chief Economist, Rocky Mountain

Research Station, USDA Forest Service,

Fort Collins, CO.

2Professor, Department of Forest

Sciences, Colorado State University, Fort

Collins, CO.
368 USDA Forest Service Proceedings RMRS-P-29. 2003.

systems have also been developed for wildfire containment applications, such

as Kourtz (1987), Saveland et al. (1988), Fried and Gilless (1989), Stock et

al. (1996), and Hirsch et al. (1998).

Introduction

Recent interest in fire and fuel management is particularly motivated by the

high fuel levels in many forested areas, especially in ecosystems with short fire

return intervals and where fire was excluded during the 20th century (Arno

and Brown 1991, Covington and Moore 1994). These fuel conditions create

a need for the long-term reduction of fuel loads. This management problem

will require new and spatially explicit management science methods expanded

to landscape scales. Most approaches in the current literature boil down to a

greedy algorithm selecting areas according to some ranking of risk or effectiveness (e.g., Chew et al. in press, Omi et al. 1981). In this paper, we will

explore a few options for attacking this problem that are a bit richer in either

dynamic or spatial relationships, and point out the significant difficulties along

the way. First, we will look at approaches for creating and maintaining a prespecified set of forest conditions that are deemed desirable from a fuels

management perspective. Second, we will look at capturing the spatial relationships suggested by a fire event targeted for long term fuels management.

Creating and Maintaining a Desired Forest

A number of authors have suggested that fire and fuels conditions in ponderosa pine and mixed conifer forests would be improved by creating a forest

with densities and age structures that emulate historical conditions or natural

processes (e.g., Brown et al. 1999, Keifer et al. 2000). A forest’s fuel profile is

quite complex, but let us assume that a pre-specified density and age structure

can be defined that is “desirable” from a fuels management perspective. Scheduling the removals (through controlled burning, mechanical removals,

commercial timber harvests, and other methods) to create and maintain such

a forest presents the forest management scientist with a formidable problem

in and of itself. Let us begin with the traditional timber harvest scheduling

models that focused on removals to optimize some measure of present net

worth.

Traditional Timber Harvest Scheduling Models

Traditional approaches to even-aged timber harvest (and other removals)

scheduling are summarized in Johnson and Scheurman (1977). Using the

“Model I” as the most typical formulation, the approach defines discrete time

periods and limits to the age of harvest, so as to create a finite number of

scheduling options on specified units of land. Assuming that the objective

function is discounted net revenue maximization, the problem is typically formulated as:

Maximize:

t

p

i

m

i

n

t

p

RL r C r ijt ij

T

X T

Ht

t

i t

= ===

Â ( ) - ( ) + -ÂÂÂ ( ) +

1 111

/ / 1 1

Hof and Omi Scheduling Removals for Fuels Management
USDA Forest Service Proceedings RMRS-P-29. 2003. 369

Subject to:

j=1

n

ij i

i

Â XA i £ "

i=1

m

j=1

n

ijt ij t

i

Â Â VX H i = "

Where:

Indexes

t indexes time periods

i indexes analysis areas

j indexes management regimes

p = number of time periods in the planning horizon

m = number of analysis areas

ni

= number of harvesting regimes for analysis area i

Variables

Ht = total harvest in the time period t

Xij = number of acres in analysis area i allocated to management regime j

Parameters

Ai

= number of acres in analysis area i

Vijt= the yield volume per acre in analysis area i, in harvesting regime j, and in

time period t

Tt

= the time (in years) at the midpoint of time period t

r = the discount rate

R = the nominal revenue per unit of volume harvested

L = the nominal cost per unit of volume harvested

Cijt = the nominal per-acre cost in time period t for analysis area i and harvest

regime j

Notice that discrete removal regimes with a limited number of management actions are defined with discrete time periods, and yields are approximated

accordingly. It should also be noted that this approach is quite conducive to

constraints such as nondeclining yield, formulated as:

H H t= p t t £ +1 1,..., -1

If we were to try to use this approach to create and maintain the desired

forest for fuels management purposes, we would probably start by defining

our land units at a fairly fine scale (so that they are relatively spatially explicit).

Then, state variables ( ikt S ) would be defined that track the number of trees in

different age classes (k ) in each land unit (i ), and each time period (t ). If

each management regime (Xij ) were defined such that specific-aged trees are

removed, then the state variables could be defined as some function of the

management regimes:

S f X i, j,k,t ikt ij = ( ) "

Scheduling Removals for Fuels Management Hof and Omi
370 USDA Forest Service Proceedings RMRS-P-29. 2003.

and the state variables could be used in objective functions or constraints to

optimize the creation and maintenance of the desired forest from a fuel management perspective.

The primary problem with this approach is that there is an extremely large

number of management regimes implied. For each land unit, a nearly infinite

number of different options would be possible in terms of the number of

different trees in different age classes harvested in different time periods. In

order to even approach an optimal conversion and maintenance of the desired

age structure over all the land units, the choice variables would have to include a reasonable representation of all options available. This problem arises

because the Model I formulation presented above is an even-aged management model, and the problem of creating a forest with a particular

location-specific age distribution is really an “any-aged management” problem.

An Any-Aged Management Approach

The early studies that formulated optimization models for uneven-aged

management (e.g., Adams and Ek 1974) assumed a steady state solution and

viewed the stand-level uneven-aged management problem as one of determining the optimal diameter class distribution, the optimal species mix, the

optimal cutting cycle length, and also an optimal conversion strategy for stands

not initially in the desired steady state (Hann and Bare 1979, Gove and

Fairweather 1992). The steady state assumptions make the problem more

tractable, with choice variables (based on diameter classes) and a cutting cycle

that apply across the stand.

The less restrictive approach in Haight et al. (1985) is probably more conducive to fuels management, because it does not necessarily need to be used in

the context of a steady state solution (see also, Buorngiorno and Michie 1980).

Haight (1987) and Haight and Monserud (1990) subsequently coined the

term “any-aged management” which we use here as the underlying basis for

fuels management. The problem with the model in Haight et al. is that only

relatively small problems can be solved (as it is nonlinear). For this apparent

reason, Haight et al. only apply their model to a single stand. What is needed

for fuels management is a formulation that can handle many diverse (and spatially explicit) stands and be solvable with large problems covering large areas

of land.

The Haight et al. (1985) and Buongiorno and Michie (1980) models define choice variables that directly relate to the trees removed, rather than the

area treated as in the traditional timber harvest scheduling models described

above. The Haight et al. model is nonlinear because the periodic ingrowth

and mortality of trees in each age class are nonlinear functions of the number

of trees in (potentially) all the age classes. One approach to the large-scale

fuels management problem might be to relax this assumption enough to formulate a linear program with many stands. At this point, let us abandon the

economic objective function in favor of one that directly optimizes the creation and maintenance of the desired forest. A possible formulation along

these lines might look like:

Minimize:

i

m

k

q

t

p

ikt

= ==

ÂÂÂ

1 11

l (1)

Hof and Omi Scheduling Removals for Fuels Management
USDA Forest Service Proceedings RMRS-P-29. 2003. 371

Subject to:

likt ikt ik ≥ S U- "i,k,t _ (2)

likt ik ikt ≥ U - " S i,k,t _ (3)

S S X i,k ik ik 1 1 =- " ik (4)

S S i t

k

q

ik ik t it i t r +A X it p 1 1 = ( ) - "= 2

=

Â ( ) -

1

1 ; ,..., (5)

S M S X ik q t p ikt = - ( ) ik ik ( ) - ( ) - t- - "= -= ikt Ê

Ë  ̄

1 2 12 1 1 1 ; ,..., ; ,..., (6)

S M S M S X it p iqt iq iq t = - ( ) ( ) - ( ) - - + - ( ) iq iq t( ) - - "= iqt Ê

Ë  ̄

11 2 1 1 1 1 ; ,..., (7)

Where:

Indexes

t indexes time periods (there are p of them)

i indexes analysis areas (there are m of them)

k indexes age groups (there are q of them)

Note: t and k are defined with the same time step.

Variables

Xikt= the number of trees harvested in time period t, in area i, and in age class k

Sikt= the number of trees in time period t, in area i, and in age class k

Ait= the number of trees artificially planted in area i in time period t

Parameters

rik= the natural regeneration rate for age class k in area i

Uik = the targeted number of trees in age class k in area i

Sik = the initial number of trees in age class k in area i

Mik= the mortality rate for age class k in area i

Equation (1) minimizes the sum of all deviations from the desired forest,

over all land units, age classes, and time periods. Equations (2) and (3) define

the lambda variables as the absolute value of the deviation of the state variables from the desired forest variables. Equation (4) calculates the state variables

for the first time period as the initial conditions less any removals (as choice

variables) that take place at the beginning of the first time period. It is assumed here that all removals happen at the beginning of each time period.

Removals are also accounted for in equations (6) and (7). Equations (5)-(7)

track the trees in each land unit as they move through the age classes from

time period to time period. Note that the time periods and the age classes

need to be defined with consistent time steps. Equation (5) applies to age

class 1, equation (7) applies to the oldest age class, and equation 6 applies to

all age classes in between. Natural regeneration is accounted for as a parameter and artificial planting is accounted for as a choice variable in equation (5).

Mortality is accounted for as a parameter in equations (6) and (7).

Clearly, the weakness of this formulation (in terms of creating a given age

structure) is the heroic assumption that mortality and regeneration in each

age class and each area are linear functions of the number of trees in that age

class and that area, such that M and r are fixed constants. Mortality and regeneration have always been problematic in timber harvest scheduling models

Scheduling Removals for Fuels Management Hof and Omi
372 USDA Forest Service Proceedings RMRS-P-29. 2003.

because they are affected by so many factors and because they are subject to

significant randomness. The impact of these simplifying assumptions would

vary from case to case, but it is not hard to imagine situations where they

would yield significantly incorrect results. As a simple example, imagine a case

where very high densities of young seedlings already crowd the under-story,

but equation (5) would continue to add regeneration at the same rate as if

there were no seedlings at all.

An alternative approach that might be preferable in some cases would be to

make mortality and/or regeneration a linear function of the number of trees

in multiple (perhaps all) age classes (see Buorngiorno and Michie 1980). The r and M parameters would then apply to the total number of trees in multiple

age classes, possibly the entire area. This would still, however, be a simplistic,

linear characterization of very complex processes.

The analyst might be able to repeatedly solve the model, adjusting the r

and M parameters over the planning horizon to closer match the state of the

forest in solution (in each time period). Whether such an iterative approach

would converge on a stable solution or not would, again, be expected to vary

from case to case. It does help that the purpose of the model is to come as

close as possible to a pre-specified forest. Still, if the initial state is much different from the desired state (which we would expect when the initial fuels

condition is highly undesirable), then the forest mortality and regeneration

rates for the desired state would not be very accurate during the conversion

period. The fact of the matter is that the any-aged forest management problem is much more difficult from an optimization modeling standpoint than

the even-aged problem, which means that no totally satisfactory large-scale

approach is available at this time to optimally create and maintain a pre-specified forest structure that is not even-aged.

Before concluding our discussion on creating and maintaining a desired

forest, we should reiterate that an “optimal” fuel condition would be much

more complicated to determine than the age-structure problem discussed here.

The fuel profile would be influenced by harvest practices, such as slash removal standards, as well as many forest dynamics not captured in the simple

designation of an age structure. Further, judicious fuel management may involve much more than tree removals. For example, a manager may wish to

leave trees behind for a shaded fuelbreak that requires needle-fall for periodic

maintenance prescribed burns. The problem is dauntingly complex, and we

have yet to discuss the topic of the next section—spatial relationships.

Spatial Relationships—Optimization With a

Target Fire

The previous approach is spatially explicit in that choice variables are defined with sufficient spatial detail to emulate historical conditions across the

landscape, but no spatial relationships (such as juxtaposition, proximity, fragmentation, or edge relationships) are really included. The obvious source of

spatial relationships for fuels management is the spatial nature of fire itself.

That is, the spatial layout of fuels and the spatial relationships between fuel

loads in different areas are important because they can affect fire behavior. For

example, a large conflagration involving vertically contiguous fuels at the head

of a fire front can increase the probability of that fire moving to the crowns.

Stand replacement usually results if wind, moisture, and fire intensity are conducive to sustaining such a crown-fire event. In order for an optimization

Hof and Omi Scheduling Removals for Fuels Management
USDA Forest Service Proceedings RMRS-P-29. 2003. 373

model to account for these spatial relationships, however, a particular fire event

needs to be accepted as the “target” of our fuels management efforts. Otherwise, it is uncertain where the fire of concern starts and what it does from

there. If it is truly random where a fire of concern might start and how it

might behave, then fuels management may be a spatial problem only in the

sense that there might be general spatial guidelines for the desired fuel structure that we wish to obtain (which could be included with the approach

discussed in the previous section). If, on the other hand, a particular fire can be

accepted as a target for fuels management, then the placement of fuels management effort might be optimized to account for the implied spatial relationships.

Even if we assume that we can define a particular target fire to guide our

fuels management efforts, it is still not completely clear what our specific objective should be. Traditionally, fire managers have focused on fire suppression

strategies that emphasize direct control of the fire or containment of its perimeter within pre-determined or natural barriers. When confronted with fires

that exceeded control or containment capabilities of available suppression resources, the fall-back position has called for the protection of valued resources

(i.e., homes, communities, or other human developments). This approach is

also relevant when it is decided to let a fire burn so as to restore natural fire

processes or as a part of fuels reduction efforts. Recent policy changes

(Zimmerman and Bunnell 1998) call for an expansion of strategies for managing fires, especially at the landscape scale. As Finney (2001:219-220) states,

“Two basic strategies for landscape-level fuel management are to contain fires

and to modify fire behavior...a spatial arrangement of treatments that primarily modifies fire behavior would involve area-based or dispersed patterns. For

fire modification, it is clear that the greatest reduction in fire size and severity

occurs when fuel treatment units limit fire spread in the heading direction.”

One option between letting a fire burn unhindered and attempting suppression is thus to slow its spread across the landscape, relative to any valued resources that it threatens. This is a fundamentally different objective than the

traditional approaches, and its practical application would depend on acceptance by the fire management community.

Let us assume that there are distinct areas of concern (such as towns, summer homes, campgrounds, ski areas, and so forth), and that a fire management

objective is to delay ignition of those “protection areas” in the target fire

event as long as possible. The advantages of such a delay would include: (1)

maximizing the chances that other suppression efforts or independent factors

such as weather changes might cause the fire to subside before the protection

areas are impacted; and (2) maximizing the time available for building fire line

around the protection areas, for modifying fuels to reduce a fire’s severity near

the protection areas, or for evacuation of the protection areas.

If the objective of long-term fuels management is to mitigate the effects of

a particular target fire, with known origin(s) and spread behavior, then one

approach (from Hof et al. 2000) could be as follows. To begin, the landscape

would be defined with a grid of cells to capture spatial location. The management variables would be defined as application of fuels reduction efforts in

each cell (such as prescribed burning, mechanical removals, and so forth) and

these efforts would be scheduled over a fairly long period of time because only

so much fuels reduction can be accomplished in a given year (or season).

Thus, discrete time periods of, say, one to ten years would be defined and

indexed with t. The trajectory of each cell’s fuel load over time, and its response to fuels management, would have to be tracked as well. Such a model

might be formulated as:

Maximize: 

l (8)

Scheduling Removals for Fuels Management Hof and Omi
374 USDA Forest Service Proceedings RMRS-P-29. 2003.

Subject to:

l £ " ∞ T t mnt (9)

T t abt

∞ = " 0 (10)

T T i, j, t

h, k

ijt hkt

ij

∞ £ "

"  ( ) W (11)

 

T T f F i, j, t ijt ijt ij ijt - = ( ) " o (12)

F B X i, j,t ijt

k

K

ijkt ijk

ij

= "

=

Â

1

(13)

k

K

ijk

ij

X i, j

=

Â £ "

1

1 (14)

i jk

K

ijkt ijk t

ij

ÂÂÂ DX X t £ " (15)

0 1 ££" X i, j,k ijk (16)

Where:

Indexes

i indexes cell rows, as does h

j indexes cell columns, as does k

k indexes management prescriptions (there are Kij of them for cell ij )

Variables

Tijt = the time that the target fire front ignites cell ij, if it occurs in time period t

Tijt= the time that the target fire front leaves cell ij, if it occurs in time period t

Thkt= the time that the target fire front leaves cell hk (and potentially ignites

cell ij ) if it occurs in time period t

Fijt= the fuel available for combustion in cell ij and in time period t

Xijk= the proportion of cell ij allocated to management prescription k

f

ij =an empirical function that relates available fuels in cell ij to the duration of

time between entry and exit of the fire front

Parameters

a,b = the row and column of the fire origin cell

m,n = the row and column of the protected area cell

Wij = the set of row and column indexes for cells that can potentially ignite

cell ij. Note: this would typically be some subset of the cells adjacent to

cell ij and would be determined primarily by a combination of wind conditions during the target fire and topography.

Bijkt = the available fuel in time period t, in cell ij, that results if management

prescription k is applied

Dijkt = a dummy parameter that is equal to one if management prescription k

applies fuels reduction in time period t, in cell ij, and is zero otherwise

Xt = the total number of cells that can be treated with fuels reduction in time

period t

’

’

’

’

∞

Hof and Omi Scheduling Removals for Fuels Management
USDA Forest Service Proceedings RMRS-P-29. 2003. 375

The objective function (8) together with the inequalities in (9) maximize

the minimum ignition time of the protection cell mn across time periods (the

target fire might occur in any time period). Other objective functions that

aggregate time periods would also be possible. Equation (10) sets the ignition

time of the origin cell to zero. Equation (11) relates the ignition times of each

cell to the times that the fire front leaves the cells which can potentially ignite

it. The multiple inequalities in (11) cause each cell to be ignited by the first

potentially igniting cell that the fire front departs from. Equation (12) relates

the duration of time that it takes the fire front to move through each cell to

the available fuel in that cell, given existing weather and topography. In practice, the spread rate functions (f) would be estimated with fire prediction models

or empirical data. Linear approximation of this f function is discussed in Hof

et al. (2000). Constraints (10) - (12) account for a potential target fire in each

time period. Constraint (13) applies the management variables (that include

scheduling) to determine the changing available fuel load in each cell ij over

time. Constraint (14) restricts the sum of the management prescription variables to be less than or equal to one for each cell, and constraint (15) limits

the amount of fuels management treatment that can be applied in each time

period. It may be desirable to replace (16) with binary integer constraints on

all Xijk, to force either complete treatment or no treatment of each cell in any

given time period. As the formulation stands, it is assumed that the Bijkt parameter is applicable to fractional values of Xijk. Extension of this approach to

multiple protection areas is straightforward (see Hof et al. 2000).

This model, again, is based on using a target fire to guide long term fuels

management. The approach is similar to the use of particular storm events

(such as a 500-year flood) to guide watershed and flood control planning.

Presupposing highly random fire behavior conditions such as wind speed and

direction or location of lightning strikes, however, may be far less certain than

the path of water flow on a landscape. If a different fire event eventually occurs, the fuels management strategy based on the target fire may or may not

be desirable. At any rate, the model is readily solvable if the f functions are

linear, because it is then a linear program with continuous variables.

Conclusion

Overall, long term fuels management presents a formidable problem for

management scientists. Treating the problem as one of creating and maintaining a particular forest (which is believed to be desirable from a fuels perspective

either because of historical conditions or some other criterion) assumes that a

desirable fuel profile can be obtained by creating and maintaining a particular

forest density and age structure. The resulting forest removals problem is difficult because it is an any-aged forest management problem that is intrinsically

nonlinear. The assumptions necessary to make such a problem linear are rather

heroic. Treating the problem so as to account for the spatial nature of fire

itself is difficult because fire origins and behavior can be quite random and

unpredictable. It is necessary to accept a particular fire event as the target for

fuels management. An approach that focuses on spatial fuel pattern, per se,

might show promise, but guidelines for desirable patterns are not apparent.

Monte Carlo approaches that simulate many fires might show promise in accounting for the uncertainty of fire origin and behavior, but heuristics for

finding near-optimal solutions have yet to be developed and the basic computing time necessary to adequately simulate an adequate number of fires may

Scheduling Removals for Fuels Management Hof and Omi
376 USDA Forest Service Proceedings RMRS-P-29. 2003.

be prohibitive. Clearly, much additional work is needed on all aspects of the

spatial and dynamic management of fuels at the landscape scale.

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Boychuk, D.; Martell, D.L. 1988. A Markov chain model for evaluating seasonal

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USDA Forest Service Proceedings RMRS-P-29. 2003. 379

Wildland Fire Use: A Wilderness Perspective

on Fuel Management

Carol Miller1

Abstract—Current federal wildland fire policy recognizes wildland fire as an important natural process and emphasizes the need to reintroduce fire into ecosystems.

The policy also recognizes that hazardous fuel accumulations may need to be reduced on vast acreages of land before fire can safely be returned to wildland

ecosystems. Wildland fire and fuel managers have a variety of options for reducing

fuels including wildland fire use, management-ignited prescribed fires, thinning, and

other mechanical methods. All of these options will need to be exploited to accomplish the task of reducing hazardous fuels and restoring healthy fire-dependent

ecosystems. Wildland fire use, while focusing primarily on restoring fire as a natural

process and maintaining ecosystems, has the potential to be very effective for managing fuels. It may be the most appropriate strategy in wilderness and in other remote

unroaded areas. To effectively implement wildland fire use, wildland fire managers will

need to rely on comprehensive fire management plans. The development of these plans

should include analyses needed to support the wildland fire use decision and should

consider the potential benefits from wildland fire, long-term consequences of management decisions, and impacts of decisions across large landscapes.

Introduction

Decades of effective fire suppression and land use change have led to

fuel accumulations, escalating fire behavior and spread, increased risk

to human life and property, and the deterioration of fire dependent ecosystems. The Federal Wildland Fire Policy Report of 1995 declares, “Wildland

fire, as a critical natural process, must be reintroduced into the ecosystem.”

The policy also recognizes that hazardous fuel accumulations may need to be

reduced before fire can be reintroduced. The magnitude of the hazardous fuel

problem is substantial. It is estimated that fire regimes on over half the land

under federal ownership (230 million acres) have been moderately or significantly altered from their historical range (Rocky Mountain Research Station

1999). These lands are therefore at moderate or high risk of losing key ecosystem components and may require moderate or high levels of restoration

treatment. In addition to these at-risk lands, there are areas where healthy ecosystems already exist, and treatments may be required to maintain their condition.

A wide spectrum of strategies is available for reducing accumulated fuels

and their associated risks including naturally or accidentally ignited wildland

fires, management ignited prescribed fires, and a variety of mechanical and

chemical methods (Omi 1996). The effectiveness and cost of different fuel

treatments depends on a variety of factors including: location, fuel type, size

of treatment unit, treatment method, and institutional factors (Rideout and

Omi 1995, Schuster et al. 1997, Cleaves and Haines 1997, Cleaves et al.

1999, Gonzalez-Caban 1997). From local to national levels, managers and

1Aldo Leopold Wilderness Research

Institute, Rocky Mountain Research

Station, USDA Forest Service, Missoula,

MT.
380 USDA Forest Service Proceedings RMRS-P-29. 2003.

planners are seeking to maximize the effectiveness of fuel management programs while controlling costs. In FY2001, USDA and USDI treated 2.25

million acres for hazardous fuel reduction (USDA and USDI 2002). Although

the goal for FY2002 is somewhat higher (2.4 million acres), this is only a small

fraction of the total acreage in need of treatment. A variety of factors can limit

the acres that are treated, including funding, inadequate staffing, lack of experienced and skilled personnel, unsuitable weather, and technological limitations

(Barrett et al. 2000, Cleaves et al. 2000, Miller and Landres, in prep.).

The task of reducing hazardous fuels and restoring or maintaining healthy

fire-dependent ecosystems is enormous. Despite the impressive commitment

to hazardous fuel reduction being made at the national level (USDA and USDI

2002), available resources and opportunities to use prescribed fire and mechanical methods will always be limited. Wildland fire and fuel managers will

need to employ all available options and opportunities for reducing fuels. One

such option is the use of naturally ignited wildland fire, or wildland fire use

(WFU). This paper provides a brief historical context for WFU and discusses

WFU as a potentially effective method for fuel management.

Wildland Fire Use in Wilderness

When the Secretaries of Interior and Agriculture issued the Federal Wildland Fire Management Policy and Program Review in 1995, they provided

policy direction for all federal wildland fire activities (USDA and USDI 1995).

One of the guiding principles of the new policy is that “the role of wildland

fire as an essential ecological process will be incorporated into the planning

process.” The current direction provides for allowing fires from natural ignition sources to be managed for resource benefits if an approved fire management

plan is in place (Zimmerman and Bunnell 1998).

The use of naturally ignited wildland fires to achieve resource objectives on

federal lands began in the 1970s. At that time, these fires were called Prescribed Natural Fires (PNFs); with the policy change in 1995 came the new

terminology of Wildland Fire Use (WFU). Since the early 1970s when policies were first implemented to use natural ignitions, well over 1 million acres

have been allowed to burn by either PNF or WFU on National Park Service

and Forest Service lands (S. Botti, USDI National Park Service, unpub. data;

D. Bunnell, USDA Forest Service, unpub. data). As of 2001, 85 of the 403 FS

wildernesses (excluding Alaska) have fire management plans that allow for the

use of wildland fire.

The vast majority of PNFs and WFU have occurred within federally designated

wilderness or national parks. The Wilderness Act of 1964 defines wilderness as

“an area where the earth and its community of life are untrammeled by man,” and

“which is protected and managed so as to preserve its natural conditions.” Wilderness is to be managed so that it “generally appears to have been affected primarily

by the forces of nature, with the imprint of man’s work substantially unnoticeable.” Consistent with this language and with the current understanding of

fire’s role in natural ecosystems, the wilderness policies of all four federal wilderness management agencies (NPS, USFS, FWS, BLM) recognize the

importance of fire as a natural ecological process and the desirability of restoring

the historical role of fire to wilderness ecosystems (Parsons and Landres 1998).

Suppression of lightning ignitions clearly does not allow the forces of nature to affect wilderness and therefore runs counter to the intent of the Wilderness Act. However, fire suppression has been and continues to be the

Miller Wildland Fire Use: A Wilderness Perspective on Fuel Management
USDA Forest Service Proceedings RMRS-P-29. 2003. 381

dominant fire management strategy. Indeed, in many areas suppression has resulted in conditions where the “imprint of man’s work” is quite noticeable

as large-scale successional changes and unprecedented fuel accumulations (e.g.,

Arno et al. 1997, Covington and Moore 1994, Parsons and DeBenedetti 1979).

Most of the fires suppressed in wilderness are suppressed because there is no

fire management plan that allows for WFU. Currently, only about one in five

wilderness areas have fire management plans that allow the use of natural ignitions (Parsons 2000). Even in those wildernesses where the fire management

plan allows for WFU, the majority of lightning ignitions are suppressed. For

instance, the Bob Marshall Wilderness Complex is a large wilderness area in

western Montana with a PNF/WFU program. Between 1988-1998, 80% of

the lightning ignitions in the wilderness area were suppressed (Parsons 2000). Wilderness fires are suppressed for a variety of reasons: the potential for the

fire to escape the wilderness boundary and threaten values outside of the wilderness; overextended staff and resources; the national or regional fire situation;

air quality concerns; and a complex set of political risks (Poncin 1995, Miller

and Landres, in prep.).

Wildland Fire Use for Fuel Management

In addition to its ability to help restore the natural process of fire and its

ecological role in wildland ecosystems, WFU has the potential to be an effective strategy for accomplishing fuel management objectives. The federal

wildland fire policy supports the use of wildland fire as a fuel treatment alternative (USDA and USDI 1995). Wildland fire reduces fuels through

consumption, and interrupts fuel continuity by creating vertical and horizontal gaps within and between surface fuels and crown fuels (Brown and Smith

2000). Although the ability of prescribed fire and mechanical treatments to

mitigate wildfire behavior and severity has been demonstrated (Pollet and Omi

2002, Omi and Martinson 2002), the effectiveness of WFU as a fuel treatment has not yet been formally assessed. However, many examples exist where

fire behavior appears to be affected when the fire spreads into a previously

burned area. For example, the area burned in 1996 by the Swet Fire in the

Bitterroot NF appears to have inhibited fire spread in 2000, and in Glacier

NP, the Moose Fire of 2001 burned around the area of the Anaconda Fire of

1999. These and other anecdotal examples suggest that the mosaic created

from abutting burned areas of different ages can aid in tactical fire suppression

and reduce the probability of fire escaping to lands with high values-at-risk

(van Wagtendonk 1995, Mohr and Both 1996).

In the 105 million acres of federally designated wilderness as well as on

other unroaded lands outside wilderness, WFU may be the most feasible option for reducing fuels. Reduced access to the interiors of these areas limits the

ability to apply prescribed fire, thinning, and other mechanical methods for

fuel management. Further, these more manipulative fuel treatment methods

may be inappropriate for use in designated wilderness where their use is limited by current legal and policy constraints, as well as public acceptance

(Ingalsbee 2001, Landres et al. 2001).

Planning for Wildland Fire Use Wildland fire use is only an option if an approved fire management plan

allows it (Zimmerman and Bunnell 1998). The fire management plan should

Wildland Fire Use: A Wilderness Perspective on Fuel Management Miller
382 USDA Forest Service Proceedings RMRS-P-29. 2003.

provide the information needed to support the WFU decision and should

contain comprehensive analyses of the resource and public values that may be

affected by fire. Given the time-critical nature of the WFU decision, it is essential that these analyses be done prior to the fire incident. The fire

management plan can serve as the instrument of this pre-analysis. To support

the WFU decision, this pre-analysis should consider the following:

1. Wildland fire benefits and risks. When deciding whether to manage an

ignition as WFU, the wildland fire manager needs to assess the benefits of fire

use along with its risks. For example, fire’s ecological benefits and its ability to reduce hazardous fuels must be weighed against the potential threats it poses

to human life and property. The decision to suppress a fire is made when the

potential negative consequences from fire outweigh its potential benefits.

Conversely, the WFU decision is justified when the potential benefits outweigh the risks. The fire management plan can serve a valuable role in the

WFU decision-making process by providing the wildland fire manager with

the information needed to make a balanced assessment of the risks and benefits from wildland fire (Miller et al. 2000).

2. Long term consequences. The beneficial effects of wildland fire are often

realized over much longer time scales than the negative impacts from fire.

Landscape mosaics created by fire may be able to reduce the likelihood of

property loss in the wildland urban interface but may also require many years

of successful WFU implementation. In contrast, the social impacts from fire

can occur immediately after, or even during, the fire. In evaluating an ignition

for WFU, the wildland fire manager needs to understand the long- and shortterm consequences of both WFU and continued fire suppression. A fire

management plan could be prepared using the results of ecosystem simulation

models that project future conditions. This information would allow the manager to compare the long-term consequences of his/her alternatives.

3. Landscape scales. Fire is a process that operates at large spatial scales and

fire management activities affect entire landscapes. Implementing WFU in the

interior of a large wilderness area may adversely impact air quality far outside

the wilderness boundary. Decisions to suppress ignitions that start outside the

wilderness boundary can affect the fire regime in the interior of the wilderness

by preventing the natural immigration of fires spreading into the wilderness.

To consider an ignition for WFU, a wildland fire manager needs to evaluate

the potential impacts on a variety of values across a broad geographic area. If

developed in conjunction with a Geographic Information System (GIS), the

fire management plan can be used to organize and display information about

the social, economic, cultural and ecological values that may be affected by

fire management activities. In addition, the fire management plan could contain up-to-date information about fuels and the biophysical environment that

affects fire spread—information that can be fed directly into fire behavior prediction tools (Finney 1994).

These three aspects of the pre-analysis (fire benefits, long-term perspectives, and landscape scales) will be essential for supporting a WFU decision. In

addition, they could also help link the fire management plan to the land and resource management plan. A key element of the land management planning

process is the identification of desired future conditions, and the potential

benefits from WFU could help define these conditions. A long-term, landscape scale perspective is consistent with land and resource management

planning, which is based on the principles of long-term sustainability and crossboundary integration (Committee of Scientists 1999). Ideally, the land

management plan would provide the goals and objectives for the fire management plan and these objectives could be framed in terms of long-term desired

Miller Wildland Fire Use: A Wilderness Perspective on Fuel Management
USDA Forest Service Proceedings RMRS-P-29. 2003. 383

future conditions across the management area. To complete the linkage from

the fire management plan back to the land management plan, the success of

the fire management program should be evaluated in terms of these land

management objectives. For example, the performance of a fire management

program might be measured in terms of social impacts or desired future conditions that have been identified in the land management plan (Rideout and

Botti 2002).

Summary

The task of reducing hazardous fuels and their associated risks on federal

lands is enormous. To accomplish this task, wildland fire and fuel managers

will need to utilize the full spectrum of fuel management strategies, including

wildland fire use (WFU). WFU has the potential to be very effective for managing fuels and is likely the most appropriate strategy in wilderness and in

other remote unroaded areas. The decision to manage an ignition for WFU

will hinge on the analyses contained in the fire management plan. To adequately support the WFU decision, these analyses need to consider benefits

from wildland fire, long-term consequences, and landscape scales.

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Modeling the Effects of Fuel Treatments for

the Southern Utah Fuel Management

Demonstration Project

Donald Long1, Kevin Ryan1, Rick Stratton1, Ed Mathews1, Joe Scott1,

Maureen Mislivets1

, Melanie Miller1, and Sharon Hood1

Abstract—An integrated multi-scale analysis strategy using output from a variety of

fire behavior and effects models has been developed for the Southern Utah Fuel

Treatment Demonstration Project. Broader-scale analyses at the sub-basin or subregional scale will employ the models FIREHARM and LANDSUM across the entire

study area. Sub-watershed and landscape level analyses will primarily use the FARSITE,

FlammMap, and spatial versions of NEXUS and FOFEM on selected landscapes.

Stand and polygon level analyses will be simulated using the Fire and Fuel Extension

to the Southern Utah variant of the Forest Vegetation Simulator (FFE-FVS) and nonspatial versions of FOFEM and NEXUS for representative fuel and vegetation conditions

in the study area.

Background

Many areas throughout the United States are facing the triple threat of

increasing fire intensity, increasing residential growth in areas prone

to wildland fire, and increasing suppression costs and losses. In addition, substantial changes have and are occurring in the way we plan and implement

management on the National Forests and Grasslands relative to use of wildland fire, prescribed fire, and mechanical fuel management. Past emphasis in

fire management has been on wildfire suppression and prescribed fire in support of other resources such as hazard reduction and site preparation in

harvested areas and wildlife habitat improvement. Allowing lightning fires to

burn in wilderness areas to restore natural process has also been occurring for

better than 30 years. It is only in the last five or six years that fire management

has employed prescribed burning and mechanical fuel treatments to reduce

unnatural fuel build-up in non-wilderness areas.

The 1995 Federal Wildland Fire Management Policy Review (USDA and

USDI 1995) contributed to the development of a new federal fire policy that

directs agencies to balance fire suppression capability and the use of fire to

regulate fuels and sustain healthy ecosystems. The review directed fire managers to objectively evaluate and compare fuel treatment options including

prescribed fire, thinning and other mechanical methods of fuel treatment, and

increased utilization of biomass. Following the Cerro Grande Fire of 2000,

Secretary of the Interior Bruce Babbitt and Secretary of Agriculture Dan

Glickman directed the Interagency Federal Wildland Fire Policy Review

Working Group to review the 1995 Federal Wildland Fire Management Policy

and Program Review.

1Fire Sciences Lab, Rocky Mountain

Research Station, USDA Forest Service,

Missoula, MT.
388 USDA Forest Service Proceedings RMRS-P-29. 2003.

The General Accounting Office (GAO) report of 1999 (General Accounting Office 1999) emphasized the need for the Forest Service to develop a

cohesive strategy to address catastrophic wildfire threats. This report describes

the extent and seriousness of problems related to the health of National

Forests in the Interior West, the status of efforts by the Department of

Agriculture’s Forest Service to address the most serious of these problems,

and barriers to successfully addressing these problems and options for overcoming them. The Forest Service responded with a document presenting a

potential strategy for addressing fire management issues in fire adapted ecosystems (USDA FS 2000). This cohesive strategy establishes a framework that

restores and maintains ecosystem health in fire-adapted ecosystems for priority areas across the Interior West.

In the 1998 appropriation, Congress, with the support of the Administration,

provided a more flexible funding authority to support the aggressive use of

fire and mechanical fuels treatments, with the goals of reducing the occurrence of uncharacteristically severe wildland fires and improving ecosystem

health. In granting this new funding authority, Congress expressed a concern

that “both the Forest Service and the Department of Interior lack consistent

and credible information about the fuels management situation and workload,

including information about fuel loads, conditions, risk, flammability potential, fire regimes, locations, effects on other resources, and priorities for

treatment in the context of the values to be protected.” This resulted in the

creation of the Joint Fire Science Program, a concerted effort toward addressing the “fuels problem” and providing a scientific basis for implementing fuels

management activities with a focus on activities that will lead to development

and application of tools for managers. Development of methods for fuel characterization, mapping, and assessment must include examination of both

available and needed fuel models. In turn, development of tools for managers

requires an assessment of the role of information system technology.

Southern Utah Fuel Management

Demonstration Project

The Southern Utah Fuels Management Demonstration Project is one such

project funded by the Joint Fire Sciences Program. It will creatively link current technology in a consistent and comprehensive manner to allow

comparisons of alternatives for fuel management for roughly 13 million acres

of Southern Utah and 2 million acres of Northern Arizona. The databases and

models will be used to support the planning and implementation of an integrated, interagency landscape level fuel management program for the region.

Our goal in this respect is to improve the fuel management program in Southern

Utah and a portion of Northern Arizona by establishing an interagency demonstration area. This area is undergoing rapid change in land use, which places

some urgency on the need for this approach to fuel management.

The demonstration area (figure 1) includes contiguous state and federal

lands within the administrative boundaries of the BLM, Forest Service, state

of Utah, and the National Park Service, roughly encompassing the southern

15 percent of Utah (table 1). Several agencies have intermixed land ownership

and a history of good interagency cooperation on management issues.

Southern Utah is at the ecological crossroads for much of the Western United

States. It contains steep environmental gradients. This allows us to study a

Long, Ryan, Stratton, Mathews, Scott, Mislivets, Miller, and Hood Modeling the Effects of Fuel Treatments for the Southern Utah Fuel Management Demonstration Project
USDA Forest Service Proceedings RMRS-P-29. 2003. 389

broad range of fuel and fire regimes associated with vegetation types representative of the High Plateaus of Utah, the Great Basin, the Colorado Plateau,

and the Mohave Desert. Previous mapping efforts (Homer et al. 1997) identified 29 vegetation types in the demonstration area (table 2), including various

associations of pinyon-juniper, ponderosa pine, sagebrush-grass, aspen, sprucefir, mountain shrubs, and desert shrubs. These vegetation types are similar in

species composition, stand structure, and ecologic function, so they have similar

fire regimes to vegetation types found on hundreds of millions of acres in the

11 western states. Thus fuel treatment guidelines developed in the project

have wide potential applicability.

Figure 1—Southern Utah Fuel

Management Demonstratioin Area

location.

Table 1—Percent composition of land ownership in Southern Utah study area.

Agency Percent of study area

Bureau of Land Management 49

Forest Service 23

Private 19

State Lands 7

Bureau of Indian Affairs 1

National Park Service 1

Modeling the Effects of Fuel Treatments for the Southern Utah Fuel Management Demonstration Project Long, Ryan, Stratton, Mathews, Scott, Mislivets, Miller, and Hood
390 USDA Forest Service Proceedings RMRS-P-29. 2003.

Project Objectives

This project has three main objectives. First is to develop Geographic Information System (GIS) data layers of fuel, vegetation, weather, and terrain inputs

necessary to conduct fire behavior and fire effects analysis across the entire

study area. We will use a number of computer models to accomplish this

including FARSITE (Fire Area Simulator) (Finney 1998), FlamMap (Finney

in progress), NEXUS (Scott 1999), FOFEM (First Order Fire Effects Model)

(Reinhardt et al. 1997), LANDSUM (Keane et al. 1997a), a Southern Utah

variant of the Fire and Fuel Extension to the Forest Vegetation Simulator

(FFE-FVS) (Reinhardt and Ryan 1998), and FIREHARM (Keane in progress).

In addition, we will develop criteria for landscape features and map those that

deserve important consideration in fire management decisions including

infrastructure features and other natural and cultural resource values such as

municipal watersheds and wildland urban interface areas (WUI). The second

objective is to apply and test these models on the various fuel types and weather

conditions across the study area. This objective will facilitate review and validation of model inputs and outputs. The third objective is to demonstrate the

use of these models and data at a variety of scales in order to aid in the implementation of fuel treatment activities within the study area. This includes the

development of fuel treatment guidelines for the various vegetation types.

Table 2—Percent composition of major vegetation types in Southern Utah study area from

Utah and Arizona combined GAP Analysis cover type databases (Homer et al. 1997).

Cover type Percent of study area

Pinyon-Juniper 28.4

Salt Desert Scrub 16.7

Sagebrush 11.4

Sagebrush-Grass 6.7

Grassland 5.6

Agriculture 4.0

Spruce-Fir 3.5

Blackbrush 3.2

Aspen 3.0

Oak 3

Ponderosa Pine 2.4

Creosote-Bursage 1.6

Mountain Shrub 1.6

Dry Meadow 1.5

Mountain Fir 1.4

Ponderosa Pine/Shrub 1.3

Barren 1.2

Desert Grassland 1.2

Water .7

Alpine .3

Greasewood .3

Lowland Riparian .3

Urban .3

Mountain Riparian .2

Aspen/Conifer .1

Mountain Fir/Mountain Shrub .1

Pickleweed Barrens .1

Spruce Fir-Mountain Shrub .1

Wet Meadow .1

Wetland .1

Long, Ryan, Stratton, Mathews, Scott, Mislivets, Miller, and Hood Modeling the Effects of Fuel Treatments for the Southern Utah Fuel Management Demonstration Project
USDA Forest Service Proceedings RMRS-P-29. 2003. 391

Current Efforts

Recently, a number of federal land management agencies have invested in

efforts to produce management strategies using GIS data and fire models to

reduce extreme fire effects to communities, and cultural and natural resources,

while at the same time increasing the health of ecosystems and decreasing the

probability of extreme fire events. An initial attempt at developing such a

strategy was the “Development of Coarse-Scale Data for Wildland Fire and

Fuel Management” (Schmidt et al. 2002). This effort integrated maps depicting potential natural vegetation, current cover type, historical natural fire

regimes, and current fuel condition and identified areas in the continental

United States where current fire regimes are significantly different from historic conditions and where fuel conditions are potentially suitable for treatment.

In addition, this effort also combined maps depicting housing density,

extreme fire behavior, and fire exposure from vegetation to assess potential

threat of fire to the wildland-urban interface.

The Utah BLM has used Utah GAP Analysis GIS data, created from

LANDSAT-TM data (Homer et al. 1997), to assess state level fire hazard and

model fire potential for fire planning and dispatch purposes (Wimmer et al.

2000). When combined with GIS data layers depicting population densities

and historical fire occurrence, it identified potential areas with a serious fire

threat as well as areas where detailed interagency planning and tactical analyses and treatment may be needed.

These efforts indicate areas of Southern Utah are at risk and are suitable for

fuel treatments. They provide a strategic basis for broad, regional scale programmatic direction, particularly with respect to fire suppression activities,

but lack the spatial and thematic resolution required by current state-of-theart models to prioritize and locate landscape or project-level fuel treatments.

Generally, these models require 30 m resolution datasets that describe a wide

range of fuel and vegetation attributes such as surface fuel loads, crown fuel,

density and height of vegetation, and biophysical site potential. In addition,

they do not utilize existing fire behavior and effects models to test the relative

efficacy of fuel treatments. Concurrent with these efforts, a number of land

management agencies in Utah have initiated numerous site and project level

fuel treatment projects utilizing funding provided by the National Fire Plan.

However, these efforts are not directly linked to existing strategic and forest

level planning efforts and do not integrate these programs within and across

multiple temporal and spatial scales.

Multi-Scale Fuel Treatment Analysis

Recently, a multi-scale, integrated planning approach has been identified as

a way to link broader scale fuel management plans to more site-specific project

level fuel treatment projects (Hann and Bunnell 2001). This approach maximizes efficiency at each scale and can be more successful at achieving objectives

not only at the project level but upward to the regional and national scales

(Hann and Bunnell 2001). Also, in order to rate the relative effectiveness of

fuel treatments at a variety of scales, simulating potential fire behavior and fire

effects in a modeling environment will require use of the different models at

different scales (Reinhardt et al. 2001). The Southern Utah Fuel Management

Demonstration Project will utilize a number of fire behavior and fire effects

models at what we feel is the appropriate scale (table 3). This project has

worked closely with fire managers within the study area to produce products

that will allow them to prioritize, select, and implement fire restoration projects

across a number of different spatial and temporal scales. We have sponsored

Modeling the Effects of Fuel Treatments for the Southern Utah Fuel Management Demonstration Project Long, Ryan, Stratton, Mathews, Scott, Mislivets, Miller, and Hood
392 USDA Forest Service Proceedings RMRS-P-29. 2003.

series of workshops attended by local fire managers to help us develop GIS

layers, understand vegetation succession, assign fuel attributes to vegetation

layers, and design weather scenarios to ensure these products are valid and

usable.

The coarsest scale of analysis, across a sub-basin and sub-regional geographic

extent, will require the use of a spatially explicit multiple pathway succession

model called LANDSUM to assess general trends in vegetation distribution

and fire regimes through multi-century time periods. This analysis will primarily determine departure of current fuel and vegetation conditions from

historical conditions as well as allow us to simulate potential future conditions

under changing climatic regimes. We will initially develop successional pathway models using the Vegetation Dynamics Development Tool (VDDT)

(Beukema and Kurz 1998). We will also use LANDSUM to predict the consequences of the treatment versus no treatment of fuel over somewhat shorter

time scales such as 10 years, 50 years, and 100 years within portions of the

study area. The model will be used to programmatically simulate fuel treatment prescriptions throughout the study area to reduce extreme fire events

and maintain healthy ecosystems and communities, mainly surrounding the

higher priority areas.

The model FIREHARM will characterize potential fire hazard and fire risk

for the entire study area across time periods more appropriate for operational

fire management planning by using computed spatial and temporal probabilities of extreme fire events. This will integrate 18 years of daily weather data,

from 1980-1996, with fire behavior and fire effects models to compute temporal probabilities of user-specified fire events occurring within the study area

for every polygon on the landscape. Values at risk, including communities,

natural, and cultural areas of importance in the southern Utah landscape, will

be assessed with regards to the fire hazard and risk maps.

Table 3—Fire behavior and fire effects models used in the Southern Utah Fuel Management

Demonstration Project with corresponding management application and spatial scale of application.

Model Applications Scale

LANDSUM Management strategy comparisons to determine Sub-basin/sub-regional

vegetation response and predict successional

pathways to reduce future fire intensity, crowning

potential and flame length.

FIREHARM Management strategy comparisons to assess the Sub-basin/sub-regional

probability of extreme fire effects using various

weather scenarios.

FARSITE/ Management strategy comparisons under various Sub-watershed/landscape

Flammap weather scenarios.

NEXUS Management strategy comparisons for reducing Stand/polygon and

crown fire risk including thinning, pruning, and Sub-watershed/landscape

other fuel removal.

FOFEM Management strategy comparisons to determine Stand/polygon and

optimum treatment schedules to reduce first-order Sub-watershed/landscape

-impacts on surrounding environments under

wildfire and prescribed fire conditions.

FFE-FVS Management strategy comparisons to determine Stand/polygon

optimum treatment schedules that reduce fire

intensity, tree mortality, and surface fuel loading

over time.

Long, Ryan, Stratton, Mathews, Scott, Mislivets, Miller, and Hood Modeling the Effects of Fuel Treatments for the Southern Utah Fuel Management Demonstration Project
USDA Forest Service Proceedings RMRS-P-29. 2003. 393

The middle scale of analysis, at a sub-watershed or landscape geographic

extent, will consist mainly of varying the spatial pattern of fuel and vegetation

on the landscape in order to test ways of reducing the intensity and severity of

wildfires and protecting values at risk. The spatial pattern of fuel treatments

has an important effect on disrupting fire growth across a landscape. Work by

Finney (2000) indicates that one effective means to reduce the intensity and

severity of wildfires is to treat fuel in such a way as to minimize the area burned

by a head fire. We will work with local fire managers to identify areas on the

landscape where they have planned or implemented fuel treatment projects

and use these areas for intensive modeling. We will use FARSITE and FlamMap

to game the portions of these sub-watersheds and landscapes in order to

determine the size, shape, and configuration of fuel treatments necessary to

form an effective barrier to the development of large fires or to the spread of

large fires into sensitive areas identified as having high values at risk. The

FARSITE model is a state of the art model for predicting the spread and

intensity of fires across a landscape. It is designed to model continuous fire

behavior through time using spatial data representing elevation, slope, aspect,

surface fuel model, canopy cover, crown base height, crown bulk density, and

stand height. FlamMap predicts fire behavior without the use of a fire spread

algorithm and produces maps of surface and crown fire behavior characteristics using FARSITE input data layers for a given set of weather and/or fuel

moisture data inputs for all points across the landscape simultaneously. Fuel

treatment strategies will include a method for linking or modifying the landscape with respect to low spread rate landscape polygons identified through

the use of FARSITE and FlamMap.

In addition, NEXUS will be used to determine the effect of combinations

of thinning, pruning, and fuel removal on likelihood of surface fire vs. crown

fire and FOFEM will be used to evaluate changes in fire effects (fuel consumption, smoke, tree mortality, soil heating, and mineral soil exposure) with

wildfire vs. prescribed fire conditions for these selected landscape as well. The

NEXUS model calculates instantaneous changes in fire behavior, as well as

transitions from surface fire to torching to crown fire with varying harvest,

pruning, and fuel treatment options. We are also developing a spatial variant

of NEXUS that will allow retrieval of data from GIS databases, and will produce output in a format suitable for inclusion in GIS for spatial analysis and

mapping.

Stand or polygon level analysis will consist of individual non-spatial model

simulations of the full range of fuel and vegetation conditions identified within

the study area. The Fire and Fuel Extension to the Southern Utah variant of

the Forest Vegetation Simulator will be used to model forest and woodland

fuel and vegetation types. Simulation runs will summarize fuel conditions and

associated fire behavior and effects resulting from a variety of potential fuel

treatment strategies. NEXUS will be used to determine the effect of combinations of thinning, pruning, and fuel removal on likelihood of surface fire vs.

crown fire. Simulation runs using FOFEM will be used to summarize fuel

conditions and associated fire behavior and effects resulting from a variety of

potential fuel treatment in non-forested and non-woodland vegetation types.

We will calculate first order fire effects such as fuel consumption, smoke production, and tree mortality using the FOFEM, including the use of a “batch”

mode programmed to run for multiple stands at the landscape level. We will

analyze stand or polygon scale treatment effects on long term fuel and fire

dynamics with varying treatments to determine optimum treatment schedules

the FFE-FVS model. We will be using a new regional variant that addresses

species that occur in Southern Utah and the Intermountain West.

Modeling the Effects of Fuel Treatments for the Southern Utah Fuel Management Demonstration Project Long, Ryan, Stratton, Mathews, Scott, Mislivets, Miller, and Hood
394 USDA Forest Service Proceedings RMRS-P-29. 2003.

Summary

The Southern Utah Fuel Management Demonstration Project is a Joint

Fire Sciences project that creatively links a number of fire behavior and fire

effects models to allow comparison of fuel management strategies for roughly

13 million acres of Southern Utah and 2 million acres of Northern Arizona.

The databases and models will be used to support the planning and implementation of an integrated, interagency landscape level fuel management

program for the region. A multi-scale analysis strategy using output from a

variety of fire behavior and effects models will be developed. Broader-scale

analyses at the sub-basin or sub-regional scale will employ the models

FIREHARM and LANDSUM across the entire study area. Sub-watershed

and landscape level analyses will primarily use the FARSITE, FlammMap, and

spatial versions of NEXUS and FOFEM on selected landscapes. Stand and

polygon level analyses will be simulated using the Fire and Fuel Extension to

the Southern Utah variant of the Forest Vegetation Simulator (FFE-FVS) and

non-spatial versions of FOFEM and NEXUS for representative fuel and vegetation conditions in the study area.

References

Beukema, S.J.; Kurz, W.A. 1998. Vegetation dynamics development tool user’s guide.

Version 3.0. Prepared by ESSA Technologies Ltd., Vancouver, BC. 102 pp. Pp

309-324.

Finney, Mark A. [In progress]. FlamMap. Rocky Mountain Research Station, Fire

Sciences Lab, Missoula, MT.

Finney, Mark A. 2000. Design of regular landscape fuel treatment patterns for

modifying fire growth and behavior. Forest Science. 47(2): 219-228.

Finney, Mark A. 1998. FARSITE: Fire Area Simulator - model development and

evaluation. Res. Pap. RMRS-RP-4. Ogden, UT: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Research Station. 47 p.

Hann, Wendel J.; Bunnell, David L. 2001. Multi-scale land and fire planning.

International Journal of Wildland Fire. 10: 389-403.

Homer, C.G., Ramsey, R.D., Edwards, T.C., Falconer, A. 1997. Landscape covertype modelling using multi-scene thematic mapper mosaic. Phototgrammetric

Engineering and Remote Sensing. 63: 59-67.

Keane, Robert E. [In progress]. Fire harm. Rocky Mountain Research Station, Fire

Sciences Lab, Missoula, MT.

Keane, Robert E.; Long, D.G.; Basford, D.; Levesque, B.A. 1997. Simulating

vegetation dynamics across multiple scales to assess alternative management

strategies. In: Integrating spatial information technologies for tomorrow: GIS ’97

conference proceedings; 1997 February 17-20; Fort Collins, CO : GIS World:

310-315.

Reinhardt, Elizabeth D., Keane, Robert E.; Brown, James K. 2001. Modeling fire

effects. International Journal of Wildland Fire. 10: 373-380.

Reinhardt, Elizabeth D.; Ryan, Kevin C. 1998. Analyzing the effects of management

actions including salvage, fuel treatment, and prescribed fire on fuel dynamics and

fire potential. In: Pruden, Teresa L.; Brennan, Leonard A., eds. Fire in ecosystem

management: shifting the paradigm from suppression to prescription, proceedings

of the 20th Tall Timbers fire ecology conference; 1996 May 7-10; Boise, ID.

Tallahassee, FL: Tall Timbers Research Station: 206-209.

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Reinhardt, E.D.; Keane, R.E.; Brown, J.K. 1997. First Order Fire Effects Model:

FOFEM 4.0, user’s guide. Gen. Tech. Rep. INT-GTR-344. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. 65 p.

Schmidt, Kirsten M.; Menakis, James P.; Hardy, Colin C.; Hann, Wendel J.; Bunnell,

David. 2002. Development of coarse-scale spatial data for wildland fire and fuel

management. Gen. Tech. Rep. RMRS-GTR-87. Fort Collins, CO: U.S. Department

of Agriculture, Forest Service, Rocky Mountain Research Station.

Scott, Joe H. 1999. NEXUS: A system for assessing crown fire hazard. Fire

Management Notes. 59(2): 20-24.

U.S. General Accounting Office. April 1999. Western National Forests: A cohesive

strategy is needed to address catastrophic wildfire threats. Report to the

subcommittee on forest and forest health, committee on resources, House of

Representatives. GAO/RCED-99-65.

U.S. Department of Agriculture, Forest Service. 2000. Protecting people and sustaining

resources in fire-adapted ecosystems: a cohesive strategy. The Forest Service

Management response to General Accounting Office Report, GAO/RCED99-65. 86 p.

U.S. Department of Agriculture, U.S. Department of the Interior. 2000. National

fire plan: managing the impact of wildfire on communities and the environment.

35 p.

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wildland fire management, policy and program review-final report. National

Interagency Fire Center: Boise, ID. 45 p.

Wimmer, Sheldon; Shive, John C.; Sheffey, Jack. 2000. Utah statewide fire assessment.

Proceedings Crossing the Millennium: Integrating Spatial Technologies and

Ecological Principles for a New Age in Fire Management. Joint Fire Science

Conference and Workshop; Volume 1. Boise, Idaho, 1999 June 15-17. 298 p.

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396 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 397

Fire Regime Condition Class and Associated

Data for Fire and Fuels Planning: Methods and

Applications

Wendel J. Hann1 and Diane J. Strohm2

Abstract—A pilot project was conducted in the Trout West watersheds of the Pike

National Forest in Central Colorado. Maps and interpretations were developed to

support prioritization, planning, and effects analysis for fuel and ecosystem restoration to achieve National Fire Plan Cohesive Strategy options. The area is about 65,000

hectares (135,000 acres) in size and representative of Southern Rocky Mt. Province

ponderosa pine ecosystems. Fire regime potential vegetation-fuel types, departure

from central tendency of the historical range of variability (HRV), fire regime condition class, wildfire ignition risk, wildland urban interface, fuel models, and associated

information were mapped. An analysis was conducted indicating that treatment of

about 10,000 hectares (25,000 acres) of high and moderate HRV departure areas,

and maintenance of about 2000 hectares (5000 acres) of low departure areas, could

achieve condition class 1 over a 5-year period. Treatment and maintenance focused

on a landscape design substantially reduced wildfire risk to both wildland urban

interface and ecosystems. A treatment option focused only on wildland urban interface and buffer areas did not substantially reduce risk to communities or ecosystems

when compared to the no-treatment option.

Introduction

The Pike National Forest is located in central Colorado and contains

much of the Rocky Mountain Front between Pueblo and Denver. Wildfire is a substantial risk to National Forests as well as adjacent homeowners in

the wildland urban interface. A Forest Service pilot project was conducted to

evaluate methods for mapping and interpretation of hazardous fuel and associated data for prioritization and planning of restoration projects to reduce

risks to ecosystems and people. The area selected for the project was the Trout

West watersheds located west of Colorado Springs. The ecosystems of these

watersheds are considered representative of the ponderosa pine ecosystems of

the Southern Rocky Mt. Province (Bailey 1995). These watersheds also contain considerable wildland surban interface near the community of Woodside,

Colorado.

Hann and Bunnell (2001) provide an overview of multi-scale methods for

planning and implementation of the National Fire Plan using the Forest Service cohesive strategy (USDA Forest Service 2000) guidance across multiple

scales of planning. In the overview they emphasize the importance of stepping

down the coarse-scale fire regime condition class data developed by Hardy

and others (2001) along with other key data for prioritization and planning at

finer scales. Hann and Bunnell (2001) provide definitions of the natural fire

1USDA Forest Service Fire Management,

Silver City, NM.

2Pike-San Isabel and ComancheCimarron National Forests and

Grasslands, Pueblo, CO.
398 USDA Forest Service Proceedings RMRS-P-29. 2003.

regime groups and fire regime condition classes that we refined for this study

(tables 1 and 2).

The primary objective of the Trout West pilot project was to develop the

finer scale methods and applications for fire regime condition class. In addition, a number of other variables, including wildland urban interface, wildfire

occurrence risk, and fuel models, were also developed. A suite of additional

resource and geographic variables was also used in the integrated prioritization

and planning process.

Table 1—Natural (historical) fire regime classes from Hardy et al. (2001) and Schmidt et al. (2002) as interpreted by the

authors for modeling landscape dynamics in the Trout West watersheds.

Fire Frequency

regime (mean fire

class return interval) Severity Modeling assumptions

I 0 – 35+ years, Surface Open forest, woodland, and savannah structures

Frequent and mixed maintained by frequent fire; also includes frequent

mixed severity fires that create a mosaic of different

age post-fire open forest, woodland, shrub, or herb

patches that make a mosaic of structural stages.

Mean fire interval can be greater than 35 in systems

with high temporal variation.

II 0 – 35+ years, Replacement Shrub or grasslands maintained or cycled by frequent

Frequent fire; fires kill non-sprouting shrubs which typically

regenerate and become dominant within 10-15 years;

fires remove tops of sprouting shrubs which typically

resprout and dominate within 5 years; fires typically

remove most tree regeneration.

III 35 – 100+ Mixed and surface Mosaic of different age post-fire open forest, early to

years, Infrequent mid-seral forest structural stages, and shrub or herb

dominated patches generally <40 hectares, maintained

or cycled by infrequent fire. Interval can range up to

200 years.

IV 35 – 100+ Replacement Large patches generally >40 hectares, of similar age

years, post-fire shrub or herb dominated structures, or early

Infrequent to mid-seral forest cycled by infrequent fire. Interval

can range up to 200 years.

V 200+ years Replacement, Variable size patches of shrub or herb dominated

mixed, and surface structures, or early to mid to late seral forest

depending on the type of biophysical environment.

Cycled by rare fire or other disturbance events.

Often have complex structures influenced by small gap

disturbances and understory regeneration.

Methods

Findings on assessment of methods for project and watershed scale fire

regime condition class rating were reported by Hann (2003). Use of these

methods for mapping fire regime condition class and associated variables were

initially tested in a smaller, approximately 9,000 hectare (20,000 acre) watershed restoration-planning project on the San Isabel National Forest to the

west, which resulted in a number of recommendations for improvement

(McNicoll and Hann 2003). We decided to further test and develop these

methods in the Trout West watersheds, a larger area of about 53,000 hectares

(130,000 acres) that had both different ecosystems and different types of base

vegetation, fuels, and fire data.

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 399

The project was organized through the identification and management of

10 steps. These involved:

1) Identify and map fire regime potential vegetation-fuel types (FRPVT)

2) Model historical range of variation (HRV)

3) Assess current conditions

4) Compare current vegetation-fuel conditions with central tendency of HRV

5) Compare current fire interval and severity with central tendency of HRV

6) Summarize fire regime condition class for each FRPVT

7) Summarize area to treat and maintain to achieve condition class change

8) Map wildfire occurrence risk

9) Map fuel models

10) Map the wildland urban interface (WUI)

Identify and Map Fire Regime Potential Vegetation-fuel

Types (FRPVT)

Methods for classification of natural fire regime potential vegetation-fuel

type (FRPVT) stratification were based on several criteria (Hann 2003). The

overall objective was to stratify based on identification of biophysical conditions that create substantial differences in management implications for

restoration of fire-adapted ecosystems and reduction of risk to people and

property. These key management implications involved identifying those factors that have caused substantial change in the natural fire regime conditions,

such as:

1) exclusion of fire through suppression and lack of wildland fire use that

mimics the natural regime;

Table 2—Condition classes from Hardy et al. (2001) and Schmidt et al. (2002) as interpreted by the authors for modeling

landscape dynamics and departure from historical or natural range of variability in the Trout West watersheds. Historical

range of variability (HRV) is the variability of regional or landscape composition, structure, and disturbances, during a

period of time of several cycles of the common disturbance intervals, and similar environmental gradients, referring,

for the United States, to a period prior to extensive agricultural or industrial development (synthesized from Morgan et

al.1994; Landres et al. 1999; Hann et al. 1997). Natural range of variability (NRV) - the ecological conditions and

processes within a specified area, period of time, and climate, and the variation in these conditions that would occur

without substantial influence from mechanized equipment.

Class NRV or HRV departure Description

Condition class 1 Low Vegetation composition, structure, and fuels are similar to those of

the natural regime and do not predispose the system to risk of loss

of key ecosystem components. Wildland fires are characteristic of

the natural fire regime behavior, severity, and patterns. Disturbance

agents, native species habitats, and hydrologic functions are within

the natural range of variability.

Condition class 2 Moderate Vegetation composition, structure, and fuels have moderate

departure from the natural regime and predispose the system to

risk of loss of key ecosystem components. Wildland fires are

moderately uncharacteristic compared to the natural fire regime

behaviors, severity, and patterns. Disturbance agents, native

species habitats, and hydrologic functions are outside the natural

range of variability.

Condition class 3 High Vegetation composition, structure, and fuels have high departure

from the natural regime and predispose the system to high risk

of loss of key ecosystem components. Wildland fires are highly

uncharacteristic compared to the natural fire regime behaviors,

severity, and patterns. Disturbance agents, native species habitats,

and hydrologic functions are substantially outside the natural range

of variability.

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
400 USDA Forest Service Proceedings RMRS-P-29. 2003.

2) past management (for example, harvest and grazing practices) that have

not mimicked the natural effects of fire and other disturbance regimes; and

3) exotic invasions (for example cheatgrass, knapweed, and blister rust).

Criteria recommended to aid in stratification of FRPVT include biophysical

type differences in:

1) pre-suppression fire interval group (0-35+, 35-100+, >200 years);

2) pre-suppression fire severity (surface versus replacement versus mixed);

3) upper layer lifeform potential (herbland, shrubland, woodland, forestland,

barrenlands, ag-urban);

4) dominant upper layer species complexity potential (if one, two, or greater

than two dominant species);

5) lower layer (understory) lifeform indicator species;

6) standing and down fuels - duff/litter layer potential;

7) climate (temperature/ moisture zones not associated with slope-aspect);

and

8) slope-aspect (such as flat, cool aspect slopes, and warm aspect slopes).

The Land Type Association (LTA) map was used to examine broad vegetation type differences, and then further divided topographically to reflect changes

in fire regimes. FRPVT classes were not broken down as finely as may be

available in detailed plant association or habitat type maps, as this would cause

a large number of stratifications that would not be meaningful for development of management implications. As such, the FRPVT was identified first

relative to the fire regime group and secondarily to vegetation-fuel type species indicators that are important for management implications. The key was

to define the fire regime group first, then stratify the vegetation biophysical

type associated with a fire regime group that provides the linkage for development of management treatments.

Reconnaissance transects were driven or walked to identify the elevation,

aspect, and slope breaks associated with changes in fire regime group and

associated vegetation-fuel types. Initial field classification of fire regime group

and potential vegetation type followed the methods outlined by Hann (2003).

Fire scarred trees were located and tree boring and scar counting methods

were used to nondestructively classify the pre-suppression fire interval group.

Fire scarred stumps were located and cross-sections were cut to allow more

accurate counts of the intervals between scars in order to validate the classification of the fire interval group. Initial classification of fire severity group was

assigned based on substantial presence (surface fire regime), presence (mixed

fire regime), or absence (replacement fire regime) of fire scarred trees or stumps.

In a later step, simulation modeling of historical range of variability (HRV) of

fire interval and fire severity was used to crosscheck these classifications.

Intensive fire scar cross-dating, tree ring chronology, and ground mapping

were not conducted. Methods for characterization of FRPVT and the HRV

were designed for rapid ground reconnaissance combined with use of available data, review of the literature, and comparison of historic and current

photographs for integration using simulation modeling. For this study we

decided to limit ourselves to a level of inventory and analysis effort that could

typically be expended on most fire and fuel management projects. Therefore

we decided not to impose intensive and costly methods typically used for fire

history research.

Most FRPVT(s) were mapped using GIS map query assignments to terrain

model classes of elevation, aspect, and slope. Digital elevation models (DEM)

were utilized to derive these aspect, slope, and elevation terrain models.

However, the riparian valley FRPVT could not be mapped using this process

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 401

so the land type association map was used to make this assignment. The high

elevation grassland FRPVT also could not be mapped using the terrain model

and was delineated using aerial photos and digital orthophotos. Urban polygons were also identified from photo interpretation. Lakes were available from

existing map coverages.

Model Historical Range of Variation (HRV)

The HRV for vegetation-fuel conditions, fire frequency, and fire severity

was simulated using the vegetation development dynamics tool software (VDDT)

(Beukema and Kurz 2001). We opted to use the standardized succession and

disturbance model called the “box” model in order to have an organizational

framework with predefined successional stages and disturbances (Hann 2003).

Using the “box” model we conducted numerous simulations of HRV for

vegetation-fuel class composition, fire interval group, and fire severity group

by adjusting fire, other disturbance, and succession probabilities. Landscape

conditions that would have existed before active fire suppression were simulated over a 500-year period with a climate similar to the current. Native

American influences on fire frequency and intensity were considered part of

the natural or native system (Barrett and Arno 1982). Utilizing fire scar interval counts and other historical clues from the reconnaissance transects,

probabilities of fire occurrence and succession were calculated and used as a

range of inputs to sensitivity test the models. This information was then combined with evaluation of historical and current photos, literature (Brown and

others 1999, Kaufmann 2000, Kaufmann and others 2000 and 2001), local

knowledge, and results of the sensitivity testing to determine the final combination of disturbances and succession probabilities. The final HRV was

simulated 10 times to account for variability. The key was to develop an estimate of the variation in natural landscape dynamics that would occur without

active fire suppression and other modern anthropogenic influences over a long

time period under the current climate.

We did not have an objective to attempt to simulate HRV with high accuracy or conduct extensive validation. The objective was to simply identify the

major trends of conditions and processes that occurred in HRV to use as a

broad reference for determining departure of current conditions and processes.

From this we calculated an average for the HRV class composition, fire interval, and fire severity. This average provided an estimate of the central tendency

of the HRV to be used as a reference condition for comparison with current

conditions. The methods for comparison of current conditions with the reference estimate of central tendency follow those of Clements (1934) and

Mueller-Dombois and Ellenberg (1974). Because of the lack of intensive

FRPVT specific HRV ground truth, we followed Hann (2003) in using plus

or minus 33% from the HRV average as including the typical HRV. This is a

compromise between the plus or minus 25% recommended by Keane et al.

(1996, 1997, 2002) for simulation modeling and the 80% median range recommended by Hessburg (1999) for historical photo analysis.

For each FRPVT, the “box” model classes were cross-referenced with the

current vegetation-fuel classification for cover type, size class, and canopy closure classes. The HRV composition was then calculated. This provided a

characterization for the vegetation-fuel class composition specific to the FRPVT

that could be cross-walked to the current vegetation-fuel map data. An example is provided in table 3 for the frequent surface fire regime lower elevation

undulating ponderosa pine FRPVT. In addition, the average fire interval,

amount of surface fire, replacement fire, and total fire were also determined

from this final simulation data.

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
402 USDA Forest Service Proceedings RMRS-P-29. 2003.

The valley riparian and high elevation meadows were not modeled for the

HRV because there would be little to no active management in these areas

(the grasslands are almost entirely on private land).

Assess Current Conditions

The Resource Inventory System (RIS) map coverage was used as the vegetation data source for cover type and structure. Many polygons lacked canopy

closure data; some lacked cover type. Vegetation data of this scale was not

available for private lands. Consequently, we digitized stand polygons across

private lands and populated these with photo interpreted cover type and structure to fill the missing data. Missing data in National Forest polygons were

also attributed utilizing aerial photo interpretation.

Canopy closures from the RIS maps were combined into three classes:

Shrub/herb/tree seedling (S): <5-10% sapling and larger tree cover, corresponds with HRV class A;

Open forest (O): generally 11-50% canopy closure of sapling and larger

trees, corresponds with HRV classes C or D; and

Closed forest (C): generally > 50% canopy closure of sapling and larger

trees, corresponds with HRV classes B or E.

Structural classes (as defined by size) from RIS were also combined:

Early seral (1): tree seedling, shrub, herb (attributed in RIS as Structural

Habitat Stage 1; corresponds with HRV class A;

Mid seral (2 and 3): sapling (2) and pole (3) (attributed in RIS as structural habitat stage 2 or 3; corresponds with HRV class B or C; and

Late seral (4 and 5): mature saw timber (4) and old growth (5) attributed in RIS as Structural Habitat Stage 4 or 5; corresponds with HRV

class D or E.

During the reconnaissance transects we recognized substantial Douglas-fir

tree mortality in many stands. We decided to identify stands with substantial

(>50%) recent mortality due to Tussock moth and Douglas-fir beetle. Local

insect and disease inventories indicate that these landscape scale outbreaks

were due to the much greater amount of Douglas-fir in the landscape than

would naturally occur without fire suppression. This greater amount results in

high current landscape level vulnerability to mortality, rather than the historical individual tree or group mortality in scattered stands. Although a few stands

within the landscape may have been characteristic of mortality during the HRV,

Table 3—Simulated average for each class of the “box” model during the historical range of

variation (HRV) for the frequent surface fire regime lower elevation undulating ponderosa pine

FRPVT. The average was used as the measure of central tendency for the HRV. A plus or

minus 33% variation or range of 66% was used as a measure of the range of variation.

“Box”

model Box model FRPVT current vegetation HRV %

class description class description average

A Early development Tree regeneration open/grassland 14

B Mid development closed Closed canopy pole 4

C Mid development open Open canopy pole 11

D Late development open Open canopy mid mature–mature 59

E Late development closed Closed mid mature - mature 12

F – L Did not occur in HRV Other vegetation classes

Total 100

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 403

we concluded that many stands within the landscape would be uncharacteristic of

conditions in the HRV. Unfortunately, the local forest insect and disease aerial

survey maps were mapped at too coarse a scale for stand level attribution of

mortality. Consequently, we utilized our most recent aerial photos (1997)

and local knowledge to identify those stands with substantial mortality.

In summary, the following vegetation attributes were required for the analysis:

1. Cover type

2. Canopy closure

3. Structural (size) class

4. Substantial mortality

The current average fire interval was estimated to be 1% based on the fire

occurrence data for the whole Trout West watershed area. This was determined as an average for the whole area rather than for each FRPVT because

the fire occurrence data only exists for the past 30 years and locations and net

size of fires have low accuracy. Current amount of replacement fire as a percent of total fire was estimated at 90% based on reconnaissance of recent wildfire

areas and local knowledge of fire behavior in typical stand conditions.

Compare Current Vegetation-Fuel Conditions With HRV

Utilizing a variety of GIS and other computer tools (Spatial Tools, Arcview,

Arcinfo, Excel), a frequency table was derived to depict each unique combination of FRPVT, cover type, size class, canopy cover, and mortality for the

current map coverage. Each combination was concatenated and added to a

new item labeled “Key.”

A “Class” item was then added, and each unique combination (Key) was

cross-referenced to the associated HRV Structural Class (A, B, C, D, E). In

addition to the standard HRV classes A-E, “box” model Classes I-L applies to

uncharacteristic conditions. These were not included in the HRV characteristic classes on the premise that historical conditions would have contained none

or very minimal (<1%) amounts of such types at a landscape scale:

Class G: Uncharacteristic Timber Harvest (harvesting has produced a

type that did not occur at a landscape scale in HRV);

Class I: Uncharacteristic Succession (succession has proceeded beyond

the HRV range producing a type that did not occur at a landscape

scale in HRV); and

Class L: Uncharacteristic Insect or Disease mortality (insect and disease

mortality creating a type that did not occur at a landscape scale in

HRV).

A corresponding item “Name” was created as a class descriptor, and attributed with a label, such as Open Mid-Development. An acre field was added to

depict the corresponding acreages for each Class. Utilizing Excel, a sum is

calculated within each FRPVT for each class. A similarity comparison was then

made between HRV and current vegetation-fuel conditions for each FRPVT

(table 4). This measure of similarity was developed by Clements (1934) and is

considered to be one of oldest and most straightforward measures of similarity or its inverse, which is dissimilarity (100-similarity). In this study we use

the term “departure” in the same manner as dissimilarity. The difference for

any one class was calculated as (current – HRV average)/(current + HRV

average) expressed as a percent. A departure contribution of low was considered to be within a range greater than -25% and less than +25% difference

from the average for HRV. The contribution of moderate was considered to

be less than or equal to -25%, but greater than -75%, and greater than or equal

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
404 USDA Forest Service Proceedings RMRS-P-29. 2003.

to +25%, but less than +75%, while the high class accounted for -75% to

-100% and +75% to +100%. Abundance was considered to be rare if less than

-25%, similar if between plus or minus 25%, and high if equal or greater than

25%. The general management implication for landscape scale restoration to

reduce departure would be to maintain similar classes, maintain and recruit

rare classes, and reduce the high classes.

The sum of similarity for classes was calculated. An example for the frequent surface fire regime low elevation undulating ponderosa pine (FRPVT

1) is depicted in table 4.

We would emphasize that as discussed by Hann (2003), HRV similarity or

departure, as well as fire regime condition class, are landscape and not stand

variables. Any specific fine scale pixel or stand can occur in any one of the

characteristic HRV classes (A through E). The similarity to HRV or departure

depends on how much of each of these classes occurred during HRV vs. how

much now occur.

However, in order to identify the risk that a given stand or pixel contributes

to the condition class or departure from central tendency and the management implications, we calculate additional variables that represent the Departure

Contribution and HRV Conditions for each Class (example in table 4):

Departure Contribution:

Low Contribution: Classes A-E; < ± 25% difference from HRV

Moderate Contribution: Classes A-E; > ± 25% difference from HRV;

<75%

High Contribution: Classes A-E > 75% difference from the HRV; plus

uncharacteristic types

HRV Conditions (management implications) or abundance classes:

Maintain: Classes A-E; < ± 25% difference from HRV

Similar Abundance

Recruit: Classes A-E; > -25% difference from HRV

Rare Abundance

Reduce: Classes A-E; > +25% difference from HRV

High Abundance

Restore: Classes F-I (Uncharacteristic types)

High Abundance

This difference was calculated as (current – historic)/(current + historic) * 100.

Utilizing GIS and other computer tools (Spatial Tools, Excel, Arcinfo, and

Arcview) these departure contribution classes were displayed spatially and summarized.

Table 4—Example calculation of similarity of current conditions to HRV for the frequent surface fire regime lower

elevation undulating ponderosa pine FRPVT. The similarity for any Class is the smaller of HRV or current amounts.

“Box”

model HRV Current Similarity Difference Departure Management

class % % % % contribution Abundance implication

A 14 9 9 - 22 Low Similar Maintain

B 4 5 4 + 11 Low Similar Maintain

C 11 2 2 - 69 Moderate Rare Recruit

D 59 20 20 - 49 Moderate Rare Recruit

E 12 31 12 + 44 Moderate High Reduce

F – L 0 33 0 + 100 High High Reduce

Total 100 100 47

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 405

Compare Current Fire Interval and Severity With HRV

To compare the current fire interval and severity with HRV, we followed

the method outlined by Hann (2003). Since these values are measured in

years (fire interval) and percent canopy replacement (fire severity) the similarity of historical to current can be determined by calculating a ratio of the

smaller divided by the larger (Mueller-Dombois and Ellenberg 1974). The

departure can then be calculated by subtracting the ratio from 1 and multiplying times 100. If the current interval is less than the HRV average (currently

more frequent) the current is divided by the HRV average, while if the current interval is greater than the HRV average (less frequent fire) the order is

reversed. If the current interval is determined to be still within the HRV range

for the fire regime group then the HRV average is equal to the current interval resulting in 100 % similarity. Classification of departure from the HRV

average assumes that the variation from 0 to 33 % is within HRV, while higher

values of departure are outside the HRV.

The basis for using the larger of the current interval or the HRV average as

the denominator was to provide an estimate of the proportional ratio of change

irrespective of the direction (more or less frequent). As long as variation was

allowed within the departure and condition classes to account for the HRV

variation and a rule was imposed for those FRPVT and landscapes where fire

interval was not outside of the HRV interval range, this methodology normalizes the differences.

The current fire interval probability was calculated as the current percent

occurrence divided by 100. As discussed earlier, the current fire occurrence

was estimated at 1 per cent, or a .01 probability for the Trout West landscape

or for any FRPVT in the Trout West landscape. The HRV fire interval probability was calculated by dividing 1 by the HRV mean fire interval. An example

calculation for the frequent surface fire regime low elevation gentle ponderosa

pine follows:

Current fire probability = 1/100 = .01

HRV mean fire interval = 21 years

HRV fire interval probability = 1/21 = .047

Current to historical interval similarity = (.01/.047) * 100 = 21%.

The current severity probability is the percent occurrence of current replacement fire divided by 100. This was estimated to be 90 per cent, or a .9

probability for the Trout West landscape or for any FRPVT in the Trout West

landscape. The HRV severity probability was calculated by dividing the average percent of HRV replacement fire by 100. An example calculation for the

frequent surface fire regime low elevation gentle ponderosa pine follows:

Current replacement fire probability = 90/100 = .9

HRV mean replacement fire = 24 %

HRV replacement fire probability = 24/100 = .24

Current to historical severity similarity = (.24/.90) * 100 = 27%

The combined fire interval-severity similarity was calculated as the sum divided by 2, giving each component equal weight:

Current to historical fire interval-severity similarity = (21+27)/2 = 24%.

Summarize Fire Regime Condition Class for Each FRPVT

HRV departure from central tendency for any given attribute is calculated

by subtracting the percent similarity from 100 (Hann 2003). In the frequent

surface fire regime low elevation gentle slope ponderosa pine FRPVT example:

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
406 USDA Forest Service Proceedings RMRS-P-29. 2003.

Vegetation-Fuels departure = 100 – 47 = 53%

Fire interval-severity departure = 100 –24 = 76%

The vegetation-fuel condition class was determined by calculating departure (100-sum of similarity) and classifying condition class 1 between 0-33%

(considered to be within HRV), 2 from 34-66%, and 3 from 67-100% (table

5). The estimate of treatment in FRPVT 3 was designed to move this component two condition classes, from Condition Class 3 to the upper boundary

(33% departure) of condition class 1, while the estimate in FRPVT 1, 2, and 4

were to move one condition class. This method of estimating treatment would

respond to a management scenario focused on landscape scale restoration for

reduction of risk from both wildland fires during severe fire weather conditions and risks to ecosystem sustainability of HRV departure. The choice of

use of the upper boundary of a class, the midpoint of that class, or some other

measure for the class depends on the management scenario.

Using the standard class breaks from Hann (2003) the two components

(vegetation-fuels departure and fire interval-severity departure) of the fire regime condition class were categorized as follows for the frequent surface fire

regime low elevation gentle slope ponderosa pine type:

Vegetation-fuel departure condition class = 2

HRV vegetation-fuel departure class = Moderate

Fire interval-severity condition class = 3

HRV fire interval-severity departure class = High

The intersection of the two departure points, rather than a sum and division, was used to assign the final natural fire regime condition class (Hann

2003).

Fire regime condition class = 3

HRV departure class = High

The break between frequent and infrequent fire regimes is typically an average fire interval of 35 years (figure 1 and table 1). However, this break should

Table 5—Summary of Trout West fire regime potential vegetation types (FRPVT) with associated

area, vegetation-fuel condition class (Veg-Fuel CC), vegetation-fuel departure (Veg-Fuel Dep.),

and estimates of area to treat and maintain.

Area Veg- Veg Area to Area to

FRPVT FRPVT hectares fuel fuel treat maintain Total area

code description (acres) CC dep. (acres) (acres) (Acres)

1 Gentle 16,662 2 53 3333 583 3916

ponderosa pine (41,173) (8235) (1,440) (9675)

2 Low elevation 4788 2 39 287 287 520

south aspect (11,832) (710) (576) (1286)

ponderosa pine

3 Low elevation 2934 3 73 1174 1174 1263

north aspect (7251) (2900*) (219) (3119)

ponderosa pine

-Douglas-fir

4 High elevation 25,617 2 55 5636 5636 6829

ponderosa pine (63,301) (13,926) (2947) (16,873)

–aspen

-Douglas-fir

–spruce

-lodgepole pine

Sum 50,002 2 54 10,429 2098 12,528

(123,558) (25,771) (5182) (30,953)

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 407

be increased to 50 years for regimes with high temporal variation or the median should be used rather than the mean. The break between infrequent and

rare is typically 100 years, but can range up to 200 years. Classification breaks

for fire severity are 25% between surface and mixed and 75% between mixed

and replacement. Fire regime group 1 is dominated by surface fire but contains substantial amounts of mixed fire regimes, while group 2 is limited to

replacement regimes. Fire regime group 3 is dominated by mixed fire regimes

but includes some surface fire regimes, while group 4 is limited to replacement regimes. In fire regime group 5 fires rarely occur but can range from

replacement to mixed to surface in effects. In the Trout West analysis FRPVT

1 was the only regime to classify as a frequent, surface fire regime, and this is

borderline. Given a plus or minus 33% variation, FRPVT 1, 2, and 3 could have

a fire interval between 35 and 50 years, but given they are dominated by fires that

are mixed in time they would still be classified in the frequent regime.

We would like to emphasize that the vegetation-fuel departure was based

on cover type and structure, and the departure in these attributes only act as a

proxy for departure in species composition, fuels, and mosaic patterns, which

are important components of the natural vegetation-fuel system. In a similar

vein the mean fire interval and simple classification of severity only acts as a

proxy for departure in season of fire occurrence, variation in fire occurrence,

fire behavior, and severity of fire effects, which are important components of

the natural fire interval-severity system. However, in order to keep the analysis

simple enough to complete in a short time with readily available data, the use of

vegetation cover type-structure and fire interval-severity departure appears to

be the best proxy to overall landscape departure from HRV and in fire regime

condition class. An even greater reduction in complexity and time can be accomplished by using vegetation departure alone as a proxy for all vegetation-fuel

and fire interval-severity components. However, this can be misleading, particularly for ecosystems that contain fire dependent or associated species that require

fire effects for germination or to maintain a competitive advantage (Hann 2003).

0

20

40

60

80

100

120

140

160

180

220

240

0 10 20 30 40 50 60 70 80 90 100

Replacement Fire (%)

Fire Regime Group I Frequent Surface and Mixed

Fire Regime Group II

Frequent Replacement

Fire Regime Group III

Infrequent Mixed and Surface

35

Fire Regime Group IV

Infrequent Replacement

Fire Regime Group IV

Rare Replacement, Mixed, and Surface)

200

25

Frequent

Rare

Surface 

(underburn)

Mixed Upper layer 

replacement

75

FRPVT 1 (24, 21) FRPVT 2 (30, 20) FRPVT 3 (66, 30)

FRPVT 4 (62,58)

Fire Interval (years)

i i

◆

◆

◆ ◆

i

i

Infrequent

Figure 1. Graphical representation of natural fire regime group boundaries for inputs of fire

interval (frequency) and fire severity (% replacement upper layer) showing the x and y axis

intersects of FRPVT 1 (21, 24), 2 (20, 30), 3 (30, 66), 4 (58, 62) and the Trout West landscape

(T-W-LS as a whole (40, 47).

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
408 USDA Forest Service Proceedings RMRS-P-29. 2003.

Summarize Area to Treat and Maintain

It is very useful to have some estimate of the area to treat (recruit, reduce

and restore) in order to achieve a fire regime condition class or HRV departure option. This was calculated for each FRPVT. An example is provided in

table 5. In the example the option was to improve the FRPVT to the upper

range of condition class 1 and lower HRV departure to conditions similar to

the HRV regime based on the vegetation-fuel departure. The minimum area

treated to achieve this option was calculated as follows:

Minimum area to treat = (Vegetation-fuel Departure- 0.33) * FRPVT

area, where .33 is the upper boundary of Condition Class 1.

For the frequent surface fire regime low elevation gentle ponderosa pine

FRPVT example, the calculation was: area to treat = (0.53 - 0.33) * 16,662 ha

(41,173 acres) = 3333 ha (8235 acres). Given that condition class 1 includes

the normal range of natural or historical variability, this level of treatment

mimicking natural disturbance effects would move this FRPVT to a composition with borderline similarity to the HRV.

However, this calculation of treatment does not account for the need to

maintain acres that are currently contributing to natural vegetation-fuel HRV

conditions that could be lost due to natural succession over the project implementation period. For large landscape restoration projects the project

implementation period can often range from 5 to 15 years. The amount similar to the HRV averages for classes A, B, C, and D were summed and multiplied

times the area of the FRPVT to determine area similar to HRV. The amount

from class E was not included in the calculation, because this class will not lose

area to another class due to succession over the project implementation period. The amount of area similar to the HRV averages was divided by an average

successional period for the implementation period. This successional period

was estimated from the successional rates in the “box” model to be about 50

years. The project implementation period was estimated to be about 5 years.

As an example (table 5), the amount that needs to be maintained in the frequent surface fire regime low elevation gentle ponderosa pine:

HRV similar area = (A + B + C + D similarity) * FRPVT Area

HRV similar area = (.09 + .04 + .02 + .20) * FRPVT Area

HRV similar area = (0.35 * 16,662 ha (41,173 ac.)) = 5832 ha (14410

acres)

Implementation successional period = 50/5 = 10

Area to maintain = HRV similar area / implementation successional period

Area to maintain = 5832 ha (14410 acres) / 10 = 583 ha. (1440 acres).

A simpler way to approximate amount to maintain would be to map the

area that was classified with a “maintain” management implication (same as

“similar” abundance class) in combination with moderate and high risk of

departure, which can be used as a proxy for ecological sustainability risk. However, this does not account for differences in succession rates.

The calculation of area to treat and maintain uses the vegetation-fuel departure and not the fire interval-severity departure or both. This was because the

management option was to improve sustainability of the current vegetationfuel landscape, such that habitats would be more characteristic of natural

conditions, and when wildfire occurs, the risk of uncharacteristic behavior and

effects would be much less. The overall goal was not to mimic the frequency

and behavior of the natural fire interval-severity system. However, the knowledge that this part of the system is in condition class 3 and high departure can

be used to focus wildland fire use and prescribed fire programs on this FRPVT.

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 409

Map Wildfire Occurrence Risk

This analysis was designed to quantitatively predict future fire probabilities

of wildfire occurrence based on past fire occurrences. The process was divided

into three steps. The first step was to classify ignition frequency. Historic ignition point data was incorporated into a grid composed of .25 square kilometer

cells. The ignition sources were grouped into two classes: L = Lightning and

O = Non-Lightning. The number of ignition points present in each cell were

counted and grouped into the following categories: lightning, non-lightning,

and all sources. Ignition point classes were developed based upon: 1) the total

number of ignition points in the map extent and 2) the range of total ignition

points within the cells. The ignition point classes were defined as follows: low

(L) = 1 ignition source; Moderate (M) = 2 to 3 ignition sources; and High

(H) = 4 or more ignition sources.

The second step was to attribute ignition class to the vegetation polygon

coverage. The vegetation polygon layer accounted for all ownership types

(National Forest and private). Each polygon was assigned an ignition point

class for each ignition point category (refer to the Ignition Class by Frequency

process). This process set each ignition point class to a unique numeric value

for the polygon by assigning class: H = 1000; M = 100; and L = 1. Then these

numeric point values were summed for each polygon. The sum value was then

used to classify the polygon ignition point class of: High = >999); Moderate =

>100 – 999; and Low = 0 - 100.

The third step was to classify the wildfire occurrence risk by combining the

polygon’s ignition class with its FRCC departure contribution class. An ignition class and departure contribution matrix (table 6) was used as a guide to

query for those combinations and assign the wildfire occurrence risk class to

each vegetation polygon.

Table 6—Wildfire occurrence risk class matrix formed from the combination of FRCC departure

contribution class and polygon ignition class. Polygon ignition class relative risk levels were

calculated using recent wildland fire occurrence data for the Pike and San Isabel National

Forests. Numbers of wildland fires were summarized across this larger area, the Trout West

area, by watershed, and by departure contribution class to calculate and classify relative risk

of wildland fire occurrence during the fire weather season. Final relative classes included

very high (VH), high (H), moderate (M), low (L), and non-applicable (NA).

Departure

contribution

class Polygon ignition class

N L MH

High L M H VH

Moderate L M M H

Low L L M M

None, non-applicable, Water NA NA NA NA

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm

Fuel Model Mapping

Anderson and National Fire Hazard Danger Rating System (NFDRS) fuel

models were assigned using the descriptions from Anderson (1982) and local

knowledge of fire behavior. Both fuel model classification systems focus on

the fire behavior of the fuel model. Consequently, the assignment process

focuses on the expected fire behavior of a vegetation-fuel type rather than the

specific fuel loading and distribution characteristics of the vegetation-fuel type.

Each unique combination of FRPVT, cover type, canopy closure, size class,
410 USDA Forest Service Proceedings RMRS-P-29. 2003.

and mortality class was attributed with the corresponding fuel model class

that best fit the expected fire behavior. There were about 25 to 50 unique

combinations for each FRPVT, which were too many to display for this paper.

An example set of combinations is provided in table 7 to display the assignment process. The data was displayed in both Anderson and NFDRS Fuel

Model maps. Both fuel model classifications are very coarse but are useful for

evaluating fire-planning scenarios or for use where fuels field data and custom

fuel models are not available.

Map the Wildland Urban Interface

The wildland urban interface (WUI) and associated attributes were mapped

and linked to the vegetation-fuel polygons. While mapping of WUI was not

needed for FRCC analysis, this variable was important for defining and addressing fire management issues. A land use map was used to identify if a

polygon was urban or not urban. Photo interpretation was used to identify all

non-urban polygons as to being WUI or non-WUI. The wildland urban interface was defined as a polygon having at least one house in 16 hectares (40

acres). This value was selected because of the zoning regulations in Colorado

that typically create subdivisions with one or more houses per 16 hectares (40

acres). Each WUI polygon was then attributed to a housing density class with

the following definitions: low is 1 house per 16 hectares (40 acres) to 1 house

per 2 hectares (5 acres); moderate is more than 1 house per 2 hectares

(5 acres) to 1 house per .4 hectare (1 acre); and high is >1 house per .4 hectare

(1 acre).

Once the Urban and WUI polygons were identified, a GIS buffer was created to depict areas in relatively close proximity to WUI polygons, and to

quantify how many acres of National Forest land exist in the WUI buffer

zone. The amount of buffer needed between a crowning wildland fire front

and the urban interface varies depending both on fuels and fire weather conditions and the values of local residents. If structure protection alone is the

key value then a much narrower buffer is viable if homeowners manage for

defensible space. If local residents include values such as risk of smoke and loss

of local scenic values then this buffer should be much broader. One of the

biggest problems in effective fire management to suppress unwanted wildland

fire and protect structures and utilities occurs when suppression forces are

pinched into a narrow zone between a flaming wildland fire front and urban

areas with one-way roads, non-defensible structures, and utilities. This substantially increases safety hazards to people, property, and firefighters and limits

use of air support, equipment, and backfiring. By overlaying this buffer with

the FRCC layers, areas close to homes can be displayed that are in high

Table 7—The process for assigning Anderson and NFDRS fuels models from Anderson (1982) to

vegetation cover type, canopy closure, size class, and mortality class combinations resulted

in a large set of tabular values The data from FRPVT 1, the frequent surface fire regime lower

elevation gentle ponderosa pine, is shown in the table as an example.

Cover Canopy Size Mortality Anderson NFDRS

FRPVT type closure class class fuel model fuel model

1 Grass Shrub-grass 0 N 1 L

1 Shrub Shrub-grass 0 N 6 T

1 Ponderosa Pine Closed Pole N 8 H

1 Douglas-fir Closed Large Y 10 G

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 411

departure from natural conditions, within hazardous fuel models, and at high

risk for future wildfire occurrence.

Although there is no standard buffer width for WUI, a two-mile width has

commonly been used, as large wildfires can throw spot fires or make runs of

this distance. However, the use of a two-mile buffer resulted in nearly the

entire planning area being within the buffer, obviously defeating the intent of

identifying a priority zone. We then reduced the buffer to a one-mile width,

which was more effective in displaying a corridor around WUI areas sufficiently narrow to differentiate this high priority zone from other wildland

vegetation polygons.

Results and Discussion

Identify and Map Fire Regime Potential Vegetation Types

(FRPVT)

The Trout West planning area was stratified into six Fire Regime Potential

Vegetation Types (FRPVT). Each type represents a broad aggregate of land

with similar homogeneous fire regimes (both in historical fire interval and fire

severity) and vegetation potentials. The six different types plus designation of

urban areas and lakes included:

• FRPVT 1 – Low Elevation Gentle Slope Ponderosa Pine

Natural fire regime group I: frequent surface fires

Ponderosa pine/herb with aspen in draws

Flat to undulating topography: less than 15% slope

Montane / lower elevation: less than 8500 feet

16,669 hectares (41,173 acres)

• FRPVT 2 South Slope Low Elevation Ponderosa Pine

Natural fire regime group I: frequent mixed fires

Ponderosa pine/shrub/herb – small amount of Douglas-fir

South-facing slopes: >15% slope

Montane/lower elevation: less than 8700 feet

4790 hectares (11,832 acres)

• FRPVT 3 North Slope Low Elevation Ponderosa Pine – Douglas-fir

Natural fire regime group I – frequent mixed fires

Ponderosa pine – Douglas fir/shrub-herb

North-facing slopes: >15% slope

Montane/lower elevation: less than 8300 feet

2936 hectares (7251 acres)

• FRPVT 4 High Elevation Mixed Conifer - Aspen

Natural fire regime group III – infrequent mixed fires

Ponderosa pine-Douglas fir-aspen-lodgepole pine-spruce

Upper elevation: >8300 feet on north slopes; >8700 feet on south slopes

Montane/all aspects

25,628 hectares (63,302 acres)

• FRPVT 5 Riparian Valleys

Natural fire regime group IV –infrequent replacement fires

Valleys w/ meadow vegetation-willow-spruce – all elevations

2124 hectares (5246 acres)

• FRPVT 6 High Elevation Grasslands

Natural fire regime group II – frequent replacement fires

High elevation grassy meadows with scattered ponderosa pine

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
412 USDA Forest Service Proceedings RMRS-P-29. 2003.

Expansive meadow area specifically in the Woodland Park-Divide area

2252 hectares (5562 acres)

• URBAN

Those areas of urban influence such as shopping areas, industrial lots,

parking lots, irrigated golf courses, etc. The key is that these types do not

have sufficient vegetation-fuel to carry a wildfire nor to threaten structures. Housing developments with trees and lawns that do have sufficient

vegetation-fuel to carry a wildfire or to threaten structures were attributed as “Urban Interface” and included in the appropriate FRPVT.

311 hectares (769 acres)

• LAKES 55 hectares (135 acres)

Model Historical Range of Variation (HRV)

Historical range of variation was modeled and summarized for FRPVT

1through 4. FRPVT 5 and 6 were not modeled because there was no expected restoration or maintenance in these types. Compositions of HRV

vegetation-fuel classes, fire interval, and amount of replacement and surface

fire were summarized (tables 3, 5, and 8). Using this data each of FRPVT 1

through 4 was classified into a fire regime group (figure 1) and cross checked

with the field reconnaissance classification. FRPVT 1 on gentle slopes with a

replacement of 24% was very close to the boundary between a surface and

mixed regime, while FRPVT 2 on the steeper south slopes fell well within the

mixed regime. Both had fire intervals that appear to average about 20 years,

while FRPVT 3 on the north aspects was at the upper end of both the fire

interval class and amount of replacement for the frequent mixed group. FRPVT

4 at the higher elevations was fairly different from the other FRPVT in that it

fell well within the infrequent mixed group (58, 62) with a fire interval and

replacement levels both at about 60. These average fire intervals appear to be

somewhat more frequent than the average fire intervals identified by Kaufmann

et al. (2000a, b). This may be because we underestimated the role large herbshrub patch size with lack of seed source or competition from grasses and

shrubs in comparison to the role of fire in slowing succession back to

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications

Table 8—Summary of vegetation-fuel departure (Veg-Fuel Dep.) and fire interval-severity departure from the central

tendency measure of the HRV average for FRPVTs in Trout West watersheds.

Area Fire Fire Fire interval

FRPVT (hectares) Veg-fuel Fire interval Replacement severity -severity

Code Description (acres) dep. interval departure fire % departure departure

1 Gentle 16,662 53 21 79 24 73 76

ponderosa pine 41,173

2 Low elevation 4788 39 20 80 30 67 74

south aspect 11,832

ponderosa pine

3 Low elevation 2934 73 30 70 66 27 49

north aspect 7,251

ponderosa pine

-Douglas-fir

4 High elevation 25,617 55 58 42 62 31 37

ponderosa pine 63,301

–aspen

-Douglas-fir

–spruce

-lodgepole pine

Sum 123,558 54 40 60 47 48 54
USDA Forest Service Proceedings RMRS-P-29. 2003. 413

dominance for forested vegetation. Or it may be because the Trout West watersheds are in somewhat more gentle terrain with soils that produce more

grass and thus might have had a higher amount of fire. Given that condition

class 1 includes plus or minus 33% variation around the estimate of central

tendency for the HRV, and the difference for departure contribution, abundance, and management implication classes includes plus or minus 25%, the

disagreement with Kaufmann et al. (2000a, b) did not have substantial influence on condition class or associated variable ratings.

Assess Current Conditions

Summary of the current conditions indicate only about 67% of FRPVT 1

area is in characteristic vegetation-fuel classes, which was similar to the amount

for FRPVT 2 (tables 9 and 10). However, FRPVT 3, the north aspect ponderosa pine – Douglas-fir type had the lowest amount of characteristic types

with only 28%, while FRPVT 4 had 45% characteristic types. Most of the

uncharacteristic vegetation-fuel conditions in FRPVT 1 and 2 were a result of

succession continuing past maximum fire return intervals and generating structures that did not occur in the historical landscape. Uncharacteristic insect

and disease mortality was not a substantial factor in FRPVT 1, 2, or 4, but was

substantial (17%) in FRPVT 3. Vulnerability of stands to epidemic levels of

insect and disease mortality occurred because natural fire exclusion by suppression activities combined with historic timber harvest to reduce ponderosa

pine and allowed Douglas-fir to dominate. FRPVT 1 and 2 appear to be too

dry to have much Douglas-fir, while in FRPVT 4 much of the vulnerable or

dead Douglas-fir has been removed in past harvest or salvage. Much of the

area appears to have been affected by uncharacteristic harvest, burning, and

livestock grazing activities that occurred during the late 1800s and early 1900s

mining era. This may have contributed substantially to reduction in

Table 9—Each FRPVT was summarized for area, vegetation-fuel condition class (Veg-Fuel CC), HRV vegetation-fuel departure class, fire

interval-severity condition class (CC), the HRV fire interval-severity departure class, the fire regime condition class and the HRV departure

assignments. Condition classes were assigned as 1 for low HRV departure from central tendency, considered to be within the HRV, and

2 and 3 for moderate and high departure, considered to be increasingly outside the HRV.

Area HRV veg-fuel Fire interval- HRV Fire Fire regime HRV

FRPVT (hectares) Veg-fuel departure severity interval-severity condition departure

code Description (acres) CC class CC departure class class class

1 Gentle 16,662 2 Moderate 3 High 3 High

ponderosa 41,173

pine

2 Low elevation 4788 2 Moderate 3 High 3 High

south aspect 11,832

ponderosa

pine

3 Low elevation 2934 3 High 2 Moderate 3 High

north aspect 7,251

ponderosa

pine-Douglas

-fir

4 High elevation 25,617 2 Moderate 2 Moderate 2 Moderate

ponderosa 63,301

pine-aspen

-Douglas-fir

-spruce

-lodgepole pine

Sum 123,558 2 Moderate 2 2 Moderate

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
414 USDA Forest Service Proceedings RMRS-P-29. 2003.

ponderosa pine dominance and increase in small tree regeneration density

(McNicoll and Hann 2003).

The closed late development class dominated FRPVT 1 (31%), with open

late development also played a strong role (20%). This relationship was reversed in FRPVT 2 where the open late development was dominant (45%),

with the closed late development only having 19 percent. Succession appears

to be much slower on the south aspects that are much dryer and have more

coarse and well-drained soils than on gentle terrain. Closed (12%) and open

(13%) late development had similar amounts in FRPVT 3 apparently because

insect and disease mortality had opened many of the previously closed stands.

FRPVT 4 was dominated by the closed late development class (21%) with

only half as much open late development (12%). This type receives more moisture and has cooler temperatures causing more rapid canopy closure. Past

harvest and salvage was the primary causal agent in creating the open late

development classes.

FRPVT 1 was the only type with substantial early development (8%) vegetation. This was primarily grass and some shrub, apparently maintained in

this stage by heavy competition from the grass that limits tree seedling regeneration. FRPVT 1 and 4 were the only types having substantial mid development

closed conditions (5 and 7% respectively). In the gentle low elevation ponderosa pine type, this appeared to be related to thick “dog hair” stands created

from some past hot fire or excessive livestock grazing disturbance that maximized regeneration. In the higher elevation type, these stands were primarily

the result of past harvest followed by tree planting. None of the types contained substantial open mid development conditions.

Compare Current Vegetation-fuel Conditions With HRV

FRPVT 3 had the highest departure in vegetation-fuel conditions (figure 2,

73%). In contrast FRPVT 2 had the lowest, with only 39% departure. This is

Table 10—Summary of current vegetation-fuel class conditions to compare amount of characteristic

to uncharacteristic conditions. Characteristic vegetation-fuel classes were those considered

to have composition and structure that occurred during the HRV, while uncharacteristic classes

were considered to be those that did not occur during the HRV.

Area A-E F-L

FRPVT FRPVT hectares composition composition

code description (acres) % %

1 Gentle 16,662 67 33

ponderosa pine (41,173)

2 Low elevation 4788 68 32

south aspect (11,832)

ponderosa pine

3 Low elevation 2934 28 72

north aspect (7,251)

ponderosa pine

-Douglas-fir

4 High elevation 25,617 45 55

ponderosa pine (63,301)

–aspen

-Douglas-fir

–spruce

-lodgepole pine

Sum 50,002

123,558

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 415

interesting given these two types are on contrasting slopes. In general the

south slope has the least change because of dryer conditions that have slowed

uncharacteristic succession, with the north aspect slopes showing the greatest

departure because of moist conditions that allow fairly rapid uncharacteristic

succession combined with a lack of past harvest due to the steep terrain.

FRPVT 1 and 4 had intermediate departures (53% and 55% respectively)

that were similar to the landscape average for Trout West (figure 2, T-W-LS

54%). Departure in FRPVT 1 was somewhat less because of fairly slow uncharacteristic succession due to grass competition with tree regeneration and

some past prescribed fire and harvest treatments. Although succession may be

faster in FRPVT 4, the departure was slower because natural fire intervals are

infrequent as compared to the more frequent interval in FRPVT 1 (figure 3). In

addition, past harvests, salvage, and prescribed fire reduced departure to some

degree.

Compare Current Fire Interval and Severity with HRV

In contrast to vegetation-fuel conditions, FRPVT 1 had the highest departure in fire interval-severity (figure 2, 76%). FRPVT 2 had almost as high a

departure level with 74%. This is a logical relationship since both types are in

a frequent fire regime and the primary causal agent of departure is fire exclusion. Past excessive livestock grazing in the late 1800s and early 1900s could

have also been a related factor by reducing fine fuels, thus increasing the

natural fire interval even prior to the active fire suppression efforts initiated in

the 1920s and ‘30s. These causal factors may be interacting with increased fire

ignitions from mining-related burning that occurred during that time and

with decreased fire ignitions from Native American burning. However, given

Figure 2. Graphical representation of fire regime condition class boundaries for inputs of

vegetation-fuel departure and fire interval-severity departure showing the x and y axis intersects

of FRPVT 1 (53, 76), 2 (39, 74), 3 (73, 49), 4 (55, 37) and the Trout West landscape (T-W-LS as

a whole (54, 54). Condition class 1 can contain plus or minus 33 % variation around the

estimate of central tendency for the natural or historical range of variability. This allows for a

66% range in variation. Condition class 2 and 3 are considered to be outside the natural or

historical range of variability in successively higher levels.

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
416 USDA Forest Service Proceedings RMRS-P-29. 2003.

N

S

W E

1012

1:50000

3 4 5 Miles

FRPVT1 - Low elev / gentle slope P. Pine (FRG I) - 41,173 acres

FRPVT2 - S. Slope / low elev. Ponderosa (FRG I) - 11,832 acres

FRPVT3 - N. Slope / low elev. DF-PP (FRG I) - 7,251 acres

FRPVT4 - High elevation PP-DF-ASP-LP-SP (FRG III) - 63,302 acres

FRPVT5 - Riparian valleys - 5,246 acres

FRPVT6 - High elevation meadows - 5,562 acres

Lakes - 135 acres

Urban - 769 acres

the amount of lightning in the Trout West area, the lack of fine fuel from

excessive livestock grazing would likely be the driving force in increasing the

fire interval, as compared to changes in amounts of cultural burning.

FRPVT 3 and 4 had lower levels of fire interval-severity departure (figure

2, 49% and 37% respectively). This follows with these types having less frequent fire intervals. Although FRPVT 3 classified into the frequent fire regime,

the average (table 8, 30 years) is very close to the upper boundary for the

frequent regime. The range in variability of this type would take it into the

infrequent regime for some cycles.

One of the key effects of the departure in fire interval-severity appears to be

related to native plant diversity. The understory of mid and late development

stands and of the early development stands appears to have very low diversity

of native herb and shrub species. Many of these species are fire adapted or fire

Figure 3. Map depicting fire regime

potential vegetation types for the

Trout West watersheds of central

Colorado. Potential vegetation types

are classified based on potential

lifeform and biophysical site

indicator species. These types are

then split using terrain (aspect, slope,

and elevation) to stratify the fire

regime.

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 417

associated in terms of regeneration mechanisms. Even though the vegetationfuel class conditions may allow for these species, the lack of regenerative fire

effects precludes their development.

Summarize Fire Regime Condition Class for each FRPVT

FRPVT1 had the highest departure (76%) in fire interval-severity, but only

moderate departure (53%) in vegetation-fuel class composition (figure 2) thus

classifying as a condition class 2 for vegetation-fuel conditions, 3 for fire interval-severity conditions, and overall a fire regime condition class 3 for combined

conditions (tables 8 and 9). FRPVT 4 had the lowest departure in both components (55%, 37%) with a moderate departure and overall condition class 2

assignment. FRPVT 3 had the highest departure in vegetation-fuel class composition (73%), only a moderate departure in fire interval-severity (49%), and

thus an overall class 3 assignment.

FRPVT 1 and 2 have sufficient departure in fire interval and severity to

classify as condition class 3 although the vegetation and fuel departure would

classify as condition class 2. This would likely have implications that this regime may lack natural fire effects and have lost composition of fire associated

species. FRPVT 3 has sufficient departure in vegetation and fuel to classify as

condition class 3 although fire interval and severity departure would classify as

condition class 2. This would have implications for high fuel loading and loss

of natural cover type and structure diversity. FRPVT 4 classified as condition

class 2 for both types of departure and because of its large area extent caused

the average departure for Trout West as a whole to classify in condition class 2.

Summarize Area to Treat and Maintain

One scenario for the Trout West watersheds was to treat and maintain enough

area to change the condition to a class 1 in a landscape pattern that would reduce

risk to the urban interface. Additional secondary options included reducing potential

large fire suppression costs and reducing ecosystem risks to air, water, native species habitats, and sustainability. Given that fire interval-severity outcomes are very

difficult to measure and evaluate, it appeared that the vegetation-fuel condition

class would be the most useful indicator to estimate area to treat and maintain,

and to monitor relative to achievement of an option. Focusing on an option resulted in the need to calculate the area to treat and maintain based on the option

of changing the Vegetation-fuel condition class from 2 to 1 for FRPVT 1, 2, and

4, and from 3 to 1 for FRPVT 3. This focus on vegetation-fuel condition class

does not de-emphasize the need to focus on the fire interval-severity condition

class and departure. The fire interval-severity condition class was identified as a

focus for identification of type of treatments, particularly prescribed fire as a tool

for treatment and maintenance of polygons in FRPVT 1 and 2.

Summary of the area to treat indicated approximately 10,000 hectares

(76,000 acres) in order to achieve the condition class option (table 5). In

addition about 2100 hectares (5200 acres) would need to be maintained

during a typical project implementation period. A little over half would be

focused at FRPVT 4, about one third to FRPVT 1, with the rest in FRPVT 2

and 3. Given that 2 and 3 are located on the steeper slopes with less road

access, a strategy may be developed to treat these with prescribed fire or wildland fire use following treatment of surrounding areas in FRPVT 1 and 4 with

mechanical and prescribed fire, and only emphasizing mechanical or hand treatment where FRPVT 2 and 3 polygons abut urban interface areas. Treatment

polygons would be focused at reducing high and moderate departure (figure

4) and maintaining low departure. Given there is much more high and moderate departure (45,593 hectares, 112,663 acres) than needed to achieve the

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
418 USDA Forest Service Proceedings RMRS-P-29. 2003.

outcome, polygons would be prioritized based on most effective design to

reduce risks of wildfire to urban interface and ecosystems combined with

operational considerations (such as access, soils, terrain, and visuals). Treatment and maintenance prescriptions can be focused at those needed to reduce

certain types, recruit other types, and maintain low departure conditions.

Fuel Model Mapping

Fuel models in themselves do not indicate potential for uncharacteristic

wildfire behavior and effects, fire regime condition class, or departure from

natural (historical) conditions (Hann and Bunnell 2001). However, the combination of an indicator of departure such as in figure 4 with fuel models

(figure 5) has considerable value. Fuel model 2 (open grassy forest) would

have been the most common fuel model in the natural (historical) regime.

This fuel model can have rapid rates of spread in grassy fuels, but typically

does not crown, have potential for blowup fire behavior, have severe fire effects, throw mass firebrands, and spread with long distance spotting fires. This

fuel model still exists in scattered polygons (figure 5) but has been replaced in

most polygons with fuel models 8, 9, and 10. Fuel model 8 (closed short

needle single and multi-layer young forest without heavy ground fuels) in a

moist or cold forest setting does not have high potential for ignition, spread,

and crown fire. However, this fuel model would be uncharacteristic in a forest

setting that is subject to drought conditions. In this kind of setting this fuel

model can exhibit extreme crown fire behavior and long distance spotting

(1.5-3 km, 1-2 miles), such as occurred during the fire seasons of 1988, 1994,

and 2000. Fuel model 9 (closed long needle forest with litter-duff) can display

even more extreme fire behavior than fuel model 8 in the dry forest setting.

Fuel model 10 (closed forest with heavy ground and ladder fuels) typically

displays the most extreme fire behavior and long distance spotting. The current vegetation-fuel conditions in the Trout West watersheds produce fuel

model 8-9-10 complexes that are associated with high departure and uncharacteristic vegetation-fuel conditions.

Fuel models have shifted from the historical dominance of fuel models 2, 9,

1, and 8 to the current dominance of fuel models 8, 9, 2, and 10. This has

resulted in a fire behavior shift during severe fire weather conditions from

what were historically fast moving, but low intensity mixed and surface fires to

current fast moving, but high intensity crown replacement fires and mixed

fires. One of the biggest additional differences that affect landscape scale fire

behavior is the current lack of non-forest fire maintained herbaceous-shrub

(grass, forb, shrub) patches that were interspersed between the forested patches

where fire would drop to the ground (Kaufmann et al. 2000a, b).

Map Wildfire Occurrence Risk

The wildfire occurrence and uncharacteristic fire risk indicates that the likelihood of current and near future ignitions, rapid rates of spread, and resistance

to initial attack and wildfire containment would occur in the northern portion

of the Trout West watersheds in the more rugged terrain (figure 6, moderate

and high classes). The low class is strong to the southerly area of the watersheds indicating a lower likelihood that wildfires would initially ignite, be

difficult to control, and spread from these areas. However, based on the departure map (figure 4), once a wildfire ignited and spread from inside or from

adjacent watersheds uncharacteristic behavior (rapid rates of spread, crown

fire, potential blowup fire behavior, mass firebrands, and long distance spotting) would be just as severe in the southerly end of the watersheds as in the

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 419

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm

Figure 4. Map depicting departure

classes for vegetation-fuel conditions

in the Trout West watersheds of

central Colorado. The fire regime

condition class is determined for the

fire regime potential vegetation type

for a landscape or watershed area

and thus cannot be mapped to the

site, patch, or stand level. However

the contribution or risk (high,

moderate, low) of a given site, patch,

or stand to the overall departure that

is used to classify condition class can

be mapped.

northern area. Amount of wildland fire ignitions or fuel flammability were

found to not limit the current wildland fire occurrence. Initial attack to suppress wildland fires was found to be the primary cause of reduced fire occurrence

compared to historical fire occurrence.

The high wildfire risk associated with uncharacteristic vegetation-fuel conditions occurs in a dry forest environment that is subject to cumulative multi-year

drought and windy conditions with a high probability of ignition and spread

from the northerly end of the watersheds or adjacent watersheds. The ignition

and initial fire spread could come from the northerly portion of the landscape,

from the landscape to the west or from the landscape to the east, driven by

westerly or northwest winds, or Rocky Mt. Front easterly winds. The
420 USDA Forest Service Proceedings RMRS-P-29. 2003.

Figure 5. Map depicting fuel models for the Trout West watersheds of central Colorado. Fuel

models descriptions come from Anderson (1982) and are used along with weather and patch

or stand canopy and structure attributes for modeling fire behavior (Andrews and Chase 1989,

Finney 1998).

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications

012

1 = ” 1.6 miles

3 4 Miles

Forest Fire Mgt. Area

Trout West Bdry

Model Class

0

1

2

5

6

8

9

10

3330 11818

Anderson fm Acres Final Count

0 39 64

1 1591 5338

2 5198 25055

5 244 399

6 154 159

8 3004 56237

9 3830 29591

10 1051 6680

N

S

W E

landscape as a whole, and in the context of adjacent landscapes, presents a

high risk of eventually having a large wildfire event that could consume 60 to

80% of the watersheds, similar to the Buffalo Creek fire that occurred to the

north in 1994.

Map the Wildland Urban Interface

The map of the wildland urban interface (WUI) indicates most of this area

is in the southerly end of the watersheds on the higher elevation benches of
USDA Forest Service Proceedings RMRS-P-29. 2003. 421

Figure 6. Map depicting wildfire occurrence risk for the Trout West watersheds of central

Colorado. Relative risk levels were calculated using recent wildland fire occurrence data for

the Pike and San Isabel National Forests. Numbers of wildland fires were summarized across

this larger area, the Trout West area, by watershed, and by stand scale departure contribution

class to calculate and classify relative risk of wildland fire occurrence during the fire weather

season.

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm

Forest Fire Mgt. Area

Trout West Bdry

All Risk

H

M

L

012

1 = ” 1.9 miles

3 4 Miles

N

S

W E

the watersheds with some area extending to the north down the primary stream

valleys (figure 7). The one-mile buffer indicates about 17,000 hectares (42,000

acres) of public land are adjacent to the WUI. Most of the WUI is associated

with the high or moderate departure uncharacteristic vegetation-fuel conditions. Very little is associated with low departure conditions. This is consistent

with findings of Hann et al. (1997) in an area as far away as the northwestern

U.S. (Interior Columbia Basin).
422 USDA Forest Service Proceedings RMRS-P-29. 2003.

No Treatment, WUI Focus, and Landscape Focus Scenarios

An initial view of risk to WUI (figure 7) may falsely conclude that if most of

the interface and buffer area were treated to reduce fuels and uncharacteristic

fire behavior, objectives for change in overall condition class and WUI risk

reduction could be achieved. However, large wildfires in contiguous uncharacteristic fuels would not be substantially slowed with this type of treatment

(Finney and Cohen this volume). Nor would risk to urban interface be substantially reduced. In the most probable outcomes there is not much difference

in risk between no action and a WUI focus option. A large wildfire event

would spread as depicted in figure 8; most probably starting in the north end

or coming from adjacent west or east landscapes and spreading to the south,

initially pushed by winds from the west, north, or east, and then pushed by its

own fire pre-heating and drying green and dead fuels, then burning at even

higher intensities, and developing its own wind. Until the weather changes

with rain or cooler temperatures and a drop in winds, fire behavior would be

severe, of potential blowup nature, and spreading through long distance spotting. Fire suppression crews would be unable to attack this fire at the head

even if the urban interface buffer areas had been treated for crown fire and

fuel risk reduction, because of the mass fire brands raining into the area and

fire jumping lines constructed by dozer or hand crews. Mass firebrands would

potentially ignite many vulnerable structures causing most of the suppression

resources to focus on protecting structures rather than on fire suppression.

Although the WUI focused fuel treatments may not substantially change landscape level fire behavior, these treatments would somewhat reduce the severity

of post-fire effects. Where fuels had been treated in the WUI buffer zone the

severity of fire effects would be reduced to a more characteristic level within

the interior of the treated polygons. However, the exterior of the treated polygons would be subjected to extreme heating from the fire in adjacent

uncharacteristic fuels.

There is a different design option that can reduce wildfire risk to WUI and

have the added benefit of reducing risk to ecosystems at landscape scales. This

would be a landscape design. This type of design would involve treatment and

maintenance to achieve the condition class 1 landscape option across the Trout

West watersheds to change large wildfire behavior and effects. This option

would focus on treatment of high departure and maintenance of low departure polygons throughout the watersheds in a pattern most effective at slowing

large wildfire spread and reducing risk of negative ecosystem effects (Finney

and Cohen this volume; Hann and Bunnell 2001). The first set of treated polygons could focus on mechanical and prescribed fire treatment of operationally

accessible high departure polygons and maintenance of low departure polygons inside the line depicted in figure 7. The line generally surrounds both

the urban interface and the higher risk areas from a landscape perspective. The

second set of treatments would tie in the intermingled less operationally accessible high departure polygons through use of hand cutting and prescribed

fire by being able to anchor into the first set of treatments. In addition, prescribed fire with minimal mechanical or hand treatment could be used outside

the line and in a relatively small adjacent portion of the landscape to the west,

which is primarily roadless and wilderness, to reduce the potential for uncharacteristic fire spreading from or to that area. This would allow wildland fire

use or prescribed fire to be effectively used within the core of the adjacent

roadless and wilderness area. Similar treatments and maintenance could be

used outside the line and in the landscape to the east, which is a mosaic of

WUI and non-WUI, similar to Trout West. This would reduce the potential

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 423

Figure 7. Map depicting urban interface, a one mile buffer around urban interface, and

associated risks of uncharacteristic wildfire (high, moderate, low departure) for the Trout

West watersheds of central Colorado. The urban interface was considered to be the area with

one or more structures per 16 hectares (40 acres).

for uncharacteristic fire spread from that landscape to the Trout West watersheds or vice versa. In addition, the design could take into account ecosystem

objectives for reducing risks to air, water, native species habitats, and

sustainability; in essence achieving risk reduction for multiple benefits at the

same cost.

This landscape type of treatment would substantially change the behavior

and effects of a large wildfire run originating from within the Trout West

watersheds or from adjacent landscapes. Wildfire from any of these sources

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm

Urban interface

Private land (no homes)

High departure

2 0

1:50000

Moderate departure 2 Miles

Low departure

One - mile buffer around

urban interface

(41,604 acres NF land)

N

S

W E
424 USDA Forest Service Proceedings RMRS-P-29. 2003.

N

S

W E

Urban interface

Private land (no homes)

High departure

Moderate departure

Low departure

One - mile buffer around

urban interface

(41,504 acres NF land)

2 0

1:50000

2 Miles

would still spread fairly rapidly in grass and shrub surface fuels, but would

have low risk of torching and spotting and little risk of sustaining a running

crown fire. Initial attack would have a much higher chance of containing the

fire and if the fire escaped initial attack suppression efforts could contain the

fire using hand or dozer lines anchored across strategic areas. There would be

little spotting into urban interface structures, thus reducing risk to both

Figure 8. Map depicting the outcome of a Buffalo Creek type wildfire event upon the Trout

West watersheds of central Colorado. This type of fire behavior spreads through mass long

distance spotting across the urban interface, one mile buffer around urban interface, and into

adjacent areas. Treatment of just the urban interface or a narrow buffer does little to improve

management options for effective fire suppression and protection of wildland urban interface

values.

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 425

vulnerable and non-vulnerable structures. We again emphasize that the vulnerability of structures primarily exists within the narrow zone of the structure

and surrounding area that typically is in the ownership of the structure owner

(Cohen 2000, Finney and Cohen this volume). However, by substantially

reducing firebrands and changing fire behavior from crown to surface, the risk

even to vulnerable structures becomes less. This type of wildfire behavior could

be managed within the availability of typical suppression resources without

having to redirect most of the resources to protection of structures. We have

generally found that even in communities with high awareness of wildfire risks

and ability of structure owners to reduce these risks with mitigation of structure vulnerability and fuel management, there is at best only about half of the

structure owners that will take action. Some redirection of suppression resources would probably be necessary to protect vulnerable structures in areas

with torching, but this would be for a small number of areas compared to the

WUI wide vulnerability that exists under the no treatment or WUI focus options. Costs of suppression would be much less under this scenario than the no

treatment or WUI focus scenario and damage to resources would be minimal.

We think it is important to emphasize that restoration of the WUI area

should not be a small area (donut hole) treated to reduce crown fire and fuel

risks, within a surrounding landscape (donut) of untreated area. This can result in wildfire behavior in the surrounding donut that presents just as high a

risk to the WUI. In a similar sense if the WUI publics also consider visual, air

quality, water, and habitat values to be important, this type of donut hole

treatment will do little to reduce the risk of loss of these values from wildfire.

The key to design of successful restoration and maintenance is to reverse the

relationship, such that the WUI area and surrounding landscapes present little

risk of sustaining a running crown fire with high severity effects. High risk fuel

conditions are generally relegated to operationally inaccessible polygons that

are embedded within low risk conditions with a substantial distance to WUI

areas.

Cost Comparison of Mapping and Analysis and Treatments

Costs of mapping and analysis were estimated to total about $10,000 (table

11). For the total area of 54,775 hectares (135,352 acres) this cost an average

of $.18 per hectare ($.07 per acre). This information for the total landscape

can be used to identify the treated acres, assess effects over the whole area for

future planning and other resource planning efforts, and edge-matched with

adjacent landscapes for broad-scale assessment to support Forest or Resource

plans. Even if the total cost is paid for by the project area to be treated to meet

the condition class 1 option (12,528 hectares, 30,953 acres), this only averages $ .80 per hectare ($ .32 per acre). This is a low cost compared to the

typical costs of treatment in WUI of $200 per hectare ($500 per acre) and

$120 per hectare ($300 per acre) in non-WUI, to have information to prioritize and plan what, where, when, and associated scenario outcomes.

We compare the costs of treatment for the three different scenarios of no

treatment, WUI focus, and landscape WUI and ecosystem focus including the

potential cost of large wildfire suppression (table 12). We developed restoration treatments costs of $988 per hectare ($400 per acre) for the WUI focus

and $494 per hectare ($200 per acre) for the landscape focus using similar

methods for estimation as from Hann et al. (2001). When assessing the amount

of WUI buffer in different risk classes, we estimate that about 6070 hectares

(15,000 acres) would be treated in this scenario as compared to 10,117 hectares (25,000 acres) for the landscape scenario. A similar approach was used to

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
426 USDA Forest Service Proceedings RMRS-P-29. 2003.

assess the costs and amount treated for maintenance (table 12). We assumed

that wood product values could be produced from about half of the restoration treatments with no product values produced from the maintenance

treatments. Based on Hann et al. (2001) we estimated the average product

value return to be about $247 per hectare ($100 per acre). Suppression costs

were estimated at $500 per acre (Hann et al. 2001) for this area without

treatment, with a size of 4048 hectares (10,000 acres) for a typical large fire

size in central Colorado. Some benefit (10% reduction) in reduction of average suppression cost was applied for the WUI focus treatment scenario, but

the gross size was assumed to be similar to the no treatment scenario. Both

reductions in average suppression cost and in wildfire size were applied to the

Table 11—Estimated costs for Trout West watersheds fire regime condition class

mapping and analysis. Vegetation data (cover type, size class, canopy closure)

for Forest Service lands were already available in the resource information

system (RIS). Some data correction was necessary and other public and private

lands data was obtained through photo interpretation.

Task Person days Cost ($)

Coordination and design 10 2,000

Field reconnaissance 10 2,000

HRV modeling 12 2,400

Current maps and GIS analysis 18 3,600

Total 50 10,000

* $200 per person day = $10,000

Table 12—Estimate of costs for planning and implementation of Trout West watersheds restoration and maintenance

comparing three different scenarios: 1) no treatment, 2) WUI focus, and 3) landscape WUI and ecosystem focus.

The cost estimates include cost estimates for a large wildfire during severe fire weather conditions for each

scenario.

WUI Landscape Landscape

No area treated WUI and area treated

treatment WUI hectares ecosystem hectares

Cost item $ focus $ (acres) focus $ (acres)

Restoration 6,000,000 6,070 5,000,000 10,117

(15,000) (25,000)

Maintenance 500,000 1012 500,000 2,023

(2,500) (5,000)

Product Value - 750,000 3,035 -1,250,000 5059

(7,500) (12,500)

Suppression 5,000,000 4,500,000 4,047 500,000 4,047

(10,000) (10,000)

Property 2,000,000 1,800,000 200,000

Burn Rehabilitation 175,000 140,000 1,619 35,000 405

(4000) (1000)

Total 7,175,000 12,190,000 4,985,000

Restoration – WUI focus $400 per acre average; Landscape focus $200 per acre average.

Maintenance – WUI focus $200 per acre average; Landscape focus at $100 per acre average.

Product values - $100 per acre for 50% of restored acres.

Suppression - $500 per acre; 10% reduction WUI focus; 90% reduction for Landscape focus.

Property - 10 structures $200,000 each; 10% reduction WUI focus; 90% reduction Landscape focus.

Burned area evaluation and rehabilitation – No treatment results in 50% severe damage with rehabilitation costs of

$35/acre; WUI focus results in 40% severe damage with rehabilitation costs of $35/acre; Landscape focus results

in 10% severe damage with rehabilitation costs of $35/acre.

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 427

landscape scenario for a combined 90% decrease. A typical large fire loss of 10

structures was assumed with a value of $200,000 each for the no treatment

scenario, with a 10% and 90% reduction in risk for the WUI focus versus

landscape focus, respectively. The net sum cost for the three scenarios was

approximately 7, 10, and 5 million dollars, respectively. Sensitivity testing of

the estimated costs and assumptions on area indicate that even with major

changes the no treatment will still be similar to the WUI focus and the landscape focus will consistently be substantially lower than the other two scenarios.

Design of Treatments to Achieve an Option

A problem that has emerged with many types of urban interface and ecosystem risk reduction restoration and maintenance treatments has been the

application of measures that do not address the issue (Hann et al. 2001).

Because of the history of timber management and silviculture in our forest

ecosystems, measures such as crown closure, stand density, size, and basal area

are commonly used to design treatments to reduce risk of uncharacteristic

wildfire behavior and effects. In a similar vein the history of range management in rangeland ecosystems has resulted in common measures such as canopy

cover, basal cover, density, and utilization. Because of this history, many treatments in forest ecosystems, with objectives for reducing risks to communities

and ecosystems, continue to be focused on a tree growth, crown closure, basal

area, or stand density measure, which may not achieve the objectives. Similarly many treatments in rangeland ecosystems with objectives for reducing

risks to communities and ecosystems become focused on shrub or herb canopy

cover or density. Measures of canopy biomass distribution, canopy depth,

canopy base height, number of tree clumps, and surface fuel and ecosystem

characteristics may be much more applicable for assessing and designing treatments to reduce crown fire potential, uncharacteristic fire behavior and effects,

and coarse-filter approach to sustaining ecosystems (Finney 1988; Hann et al.

1997 and 1998; Keane et al. 1998; Reinhardt et al. 1997; Scott and Reinhardt

2001).

In addition to selection of applicable measures, projects designed to sustain

ecosystems should avoid systematic “rules of thumb” or “one size fits all”

prescriptions across all treated polygons (Hann et al 2001). Treatments with

objectives that are very prescriptive in specifying numbers of trees by size,

snags, down logs, and distance from riparian areas without allowing for natural variation can create a systematic landscape that does not allow for the fine

scale variation needed by the diversity of native organisms and processes. Treatments designed to represent the range of historical or natural variability or

even a median range of variability must be implemented in a way that allows

for that variability. This can be achieved by prescribing variation, which may in

itself constrain natural variation. A more useful technique may be to remove

the desired amount of woody biomass and then use variation in prescribed fire

effects to create the variation in polygon features such as shape, size, numbers

of dead standing and down, litter and duff reduction, and species response.

The response should be monitored and assessed against the understanding of

natural variation. As implementation proceeds, the prescribed fire prescription should be adjusted to shift variation in effects.

Textbook or coarser scale mapping applied to project area site-specific fire

regime and condition class can result in the greatest error in outcomes. We

consistently find that these coarser scale results are not appropriate for finescale project design. More often than not, the lodgepole pine type is a mixed

or surface fire regime rather than a replacement fire regime. In a given area,

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
428 USDA Forest Service Proceedings RMRS-P-29. 2003.

the sagebrush type may be a mixed regime, rather than a replacement fire

regime. The coarse scale infrequent fire regime may be a frequent interval

regime at the finer scale.

These potential errors also apply to restoration and maintenance of the

Trout West watersheds. To avoid error in selection of measures we developed

methods focused at fire regime condition class and fuel model, combined with

cover type, canopy closure, and size class. To avoid prescriptive numbers without variation we used broad classes of canopy closure and size and focused on

the fire regime, condition class, potential fire behavior and effects, and urban

interface relationships to wildlands. To avoid the textbook or coarser scale fire

regime condition class mapping implication that “all ponderosa pine types are

frequent surface fire regimes,” we developed and applied methods to develop

site-specific fire regime condition class.

Management Implications and

Recommendations

Methods

In retrospect, it would have been advantageous to have used the standard

RIS density classes (habitat structural stage) of 10-40%; 40-70%; and 70%+

canopy closure. This would have fit in better with existing vegetation data. It

is critical that the canopy closure density classes used as a basis for modeling

HRV be the same as those used to describe current vegetation conditions. In

this case, existing RIS density data was lacking in over half of the NF polygons, and an additional 25,000 acres of private land had no vegetation data.

Since we had to do such a major renovation of the RIS tabular data to utilize

it as a depiction of current conditions, we opted to develop our own set of

density classes. This resulted in a tedious and complicated process. It is important to note that the breakpoint for canopy closure for open versus closed in

the “box” model HRV structural stages is relatively flexible for two reasons:

1) estimates of canopy closure from historical photographs and stand reconstruction have high variability; and 2) ecosystems vary in what is considered

naturally open versus closed (Hann 2003). Consequently, the canopy closure

classification should be one standardized for the current vegetation, and crossreferenced to the open and closed categories for the “box” model structural

stages.

We again emphasize that the FRCC map depicts the departure contribution across the entire FRPVT and does not apply to any one individual stand.

The natural HRV landscape includes amounts in each of Classes A-E. In the

Trout West area, FRPVT 1 has a central tendency for about 12% in Class E

(closed mature/mid-mature forest). Currently, Class E comprises 31% of the

area, nearly three times as much. It is only possible to show this entire existing

Class E component as contributing moderately towards the FRPVT departure class (this is categorized as “moderate” departure because the difference

between current and HRV is >25% and <75%). It is not possible to ascertain

that any particular Class E stand is in moderate departure, because it would

have been expected to occur with a range around the central tendency of 12%

of the landscape naturally. This gives the manager the option of deciding how

much of the existing 33% in Class E should and should not be treated based

on operational accessibility.

A “priority treatment” map may be a useful venue to display those areas

contributing significantly to overall departure that are likely most in need of

Hann and Strohm Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications
USDA Forest Service Proceedings RMRS-P-29. 2003. 429

fuels reduction treatments. This would only depict those areas with Moderate

or High departure contributions and associated “Reduce” management implications or “High” abundance. It may be helpful to distinguish between

those areas contributing to substantial departures because of

underrepresentation across the landscape, and those that are overrepresented.

Our departure or “risk” map (figure 4) depicts the entire area by low, moderate and high departure or “risk” contributions. While an important analysis

product, this may be difficult to translate into on-the-ground implications.

This is because the moderate classes are categorized as such because they are

either overrepresented or underrepresented across the landscape, and thus

may need to be either reduced or recruited. Only the overrepresented moderate class that may need active management would be depicted on the priority

treatment map. The low departure contributions would not show up in the

“Priority Treatment” map, as these are classified as “maintain” or “similar.”

However, we would also caution that this could result in managers not expending enough effort in developing restoration options for “recruitment”

that would grow large trees, produce large snags and logs, regenerate to a

different species composition, or maintain what currently has low ecological

sustainability risk and is similar to the HRV.

While stratifying WUI polygons into low, moderate, and high housing or

population densities may be helpful as a means of further prioritization of the

Urban Interface zone, we found it to be less critical than the initial attribution

as urban interface. The most accurate method of determining housing densities would be a housing map with precise point locations. A density function

could then be applied to quantify home densities to meet varying definitions.

This data, however, was not available for our analysis area. One county had no

GIS housing data at all. The other county could display private parcels and

identify how many homes were on each parcel, but could not depict the homes

spatially. GIS maps of planned housing developments would also have been

helpful, but did not exist. As an alternative, housing density for all RIS polygons meeting the minimum of one house in 16 hectares (40 acres) was

attributed through aerial photo interpretation. The drawback to tying this

attribute to a polygon is that the size of the polygon determines the minimum

threshold. For example, an 80 hectare (200 acre) lodgepole pine polygon may

have 4 houses, but it does not meet the WUI classification, as it represents

only one house in 50 acres. Where the vegetation was more dissected, the

polygons would be smaller and the houses would likely meet this minimum

threshold. Acknowledging this limitation, these WUI data were infinitely more

detailed and useable than the previously available data source that depicted

very broad housing density zones. For our purposes, it worked very well.

Because of the unique patterns of land use and housing development that

occur for different areas, the housing density classification and wildland urban

interface buffer distance may need to be locally defined. We recommend further research and assessment in other areas with different patterns of land use

and housing development before standardization of methods.

There was little doubt that use of the “box” model with standardized definitions of HRV stages, succession, and disturbances greatly reduced the time

and costs of analysis and resulted in much greater consistency between models

for different FRPVT(s). Although we have no way of determining accuracy

without an independent comparison, there was general consensus among the

interdisciplinary team that use of this type of standardized model limits the

variation to that of the ecosystem rather than to model framework, and thus

reduces potential for errors. Allowing development of models with unconstrained successional paths and disturbances would have resulted in substantial

Fire Regime Condition Class and Associated Data for Fire and Fuels Planning: Methods and Applications Hann and Strohm
430 USDA Forest Service Proceedings RMRS-P-29. 2003.

variation between and within FRPVT(s). This would be a result of “splitters

versus lumpers” as well as lack of understanding to attribute detailed succession and disturbance probabilities. The five conditions (A-E) and limited

succession and disturbance pathways were scaled at about the same level of

the understanding we could achieve from reading the local literature and conducting ground reconnaissance.

Findings

We summarize five implications from the results of this work:

1) Standardized methods for fire regime condition class that has a context to the national definitions can be cost effectively and consistently

applied at project and landscape scales across all land ownerships.

2) These methods differ substantially from those applied at the coarse

scale by Hardy et al. (2001) and Schmidt et al. (2002) because the

scale of landscape composition and structure, and associated management implications, are much finer.

3) Fire regime condition class can and should be developed from the

same basic vegetation data that are used for other resource management analyses and implications. This results in more consistent and

logical outcomes in analyses and project design.

4) The analysis of no treatment, WUI focus, and landscape focus scenarios indicates that the typical approach to focusing on WUI and

buffer areas may not be a viable option to reducing risk to communities. In contrast, a landscape focus reduces risk to communities and

ecosystems with a more effective expenditure of funds.

5) Potential errors in design and implementation of treatments to achieve

objectives for reduction of wildfire risk to communities and ecosystems can occur. These are typically associated with: a) choosing

traditional forest or range management measures versus those focused

on fuels, fire behavior and effects, and ecosystem characteristics; b)

using fixed or “one-size-fits all” treatment prescriptions at a polygon

level, rather than designing for variation in polygon outcomes across

the landscape; and c) application of textbook or coarser scale fire regime condition class findings for fine-scale project design.

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Garner, Janice L. 2000. Mapping vegetation and fuels for fire management on the

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Boreal and Montane Ecosystems”, Alberta, Canada. Proceedings published by Tall

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structures for the conterminous United States. In: L.A. Brennan, et al. (eds.)

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inland west. New York: Haworth Press, Inc.: 87-111.

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L. Bunnell. 2002. Development of coarse-scale spatial data for wildland fire and

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the Gila Wilderness, New Mexico. In: Proceedings—Symposium and Workshop

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USDA Forest Service Proceedings RMRS-P-29. 2003. 435

Closing Comments: Fire, Fuel Treatments, and

Ecological Restoration—Proper Place,

Appropriate Time

G. Thomas Zimmerman1

Introduction

The title of this conference, “Fire, Fuel Treatments, and Ecological

Restoration: Proper Place, Appropriate Time,” is indicative of a wide

range of elements critically important to ecosystem management. While in

general, it encompasses many attributes of a comprehensive fire management

program, it is also specific to the recently emerging focus on fuel treatment

and ecological restoration. This conference brings together an assembly of

individuals to discuss the range of information currently available pertaining

to the issues of treating hazardous fuel buildups and restoring healthy, functioning ecosystems. The enormity of these issues, the situation surrounding

current and future activities, and the developing program to answer questions

about these subjects cannot be overstated. These are indeed huge issues that

have been developing for nearly a century and will take massive efforts, committed workforces, and large budgets to reverse the current state of both altered

fuel complexes and ecosystem health and condition. So, this conference is not

only timely, but significant in terms of the importance and quantity of information that has been shared and discussed here.

I feel very fortunate to be able to present the closing comments at this

conference. First, I am privileged to be involved with such an important effort. Secondly, when presenting comments, one has the opportunity to discuss

personal viewpoints and is not tied to interpreting actual data. In most cases,

this tends to make preparation much easier. What I will discuss here and close

this conference with are my personal perspectives and a summary of the three

days of this conference.

The Nature of the Fuel Treatment and

Ecological Restoration Problem

Are we facing a problem? I would submit that we are not facing just a

problem, but an enormous one. All practitioners involved in wildland fire

management, and even casual observers, can attest to the magnitude and harshness of the 2000 fire season. What has caused this problem? Nearly a century

of fire exclusion combined with other land uses has served to greatly increase

fuel accumulations. This, in turn, has promoted changes in fire behavior, fire

severity, and frequency. These changes in fuel and fire dynamics have led to a

general decline in ecosystem health, which in combination with an expanding

1National Park Service, National

Interagency Fire Center, Boise, ID.
436 USDA Forest Service Proceedings RMRS-P-29. 2003.

wildland-urban interface, is seriously affecting our ability to protect both ecosystems and communities. After years of stating the compounding effects of

uncontrolled fuel accretion, attention is now focused on this problem. Our

credibility has come under scrutiny and we must demonstrate the ability to

overturn the current direction that fuel and fire dynamics are following.

Have our fuel treatment and ecosystem restoration activities developed over

the years? I would submit that yes they have developed and will continue to

do so. Indicative of these developments are the accomplishments of this conference. While here, you have heard 35 presentations in the topics of:

• Fuel treatment performance – fire hazard reduction,

• Restoration case studies and ecosystem effects,

• Treatment economics and social issues,

• Fire regime considerations,

• Landscape planning, and

• Field trips and discussion.

Over 300 people registered for this conference, and at a related conference,

the National Fire Plan Conference in Madison, WI, another 300 people were

in attendance to discuss the current state and future of fuel treatment and

ecological restoration. The response to these two conferences clearly demonstrates the importance of this subject, the changing nature of current

knowledge, and the increasing demands for additional information.

Programmatic Development–Putting the

Pieces Together

How have fuel treatment and ecosystem restoration activities developed? If

we look at the past, present, and future attributes associated with our fuel

treatment and ecological restoration programs, such as historical experience,

new initiatives, application focus, changing needs, accountability and scrutiny,

changing application focus, scheduling, scale, information acquisition, collaboration, and performance measures, we can begin to see what has shaped

this program, what current influences are, and what the guiding principles of

the future will be. If we accept these attributes as the pieces that will make up

the fuel treatment and ecological restoration programs of the future, we must

fully understand what they are and how they fit into the puzzle.

Historical Experience

Earlier in this conference, Phil Omi stated that fuel treatment began in the

1930s. The actual extent of the program at this time was limited to specific

geographic areas and accomplished only localized treatments. This activity,

however limited, provided a foundation for the future. We have built on this

experience over the years, but our efforts have almost exclusively focused on

the application of prescribed fire. Limited collective experience has been gained

with mechanical treatments, but this has resulted from support of silvicultural

practices rather than from treatment of fuels or restoration of ecosystems.

Wildland fire use, formerly categorized as prescribed natural fire, has been a

fire management strategy used by some federal agencies since 1970. This practice has served as a principal technique for ecosystem maintenance and

restoration in undeveloped areas such as large national parks and wildernesses

and represents an extremely valuable strategy for ecosystem maintenance and

restoration.

Zimmerman Closing Comments: Fire, Fuel Treatments, and Ecological Restoration—Proper Place, Appropriate Time
USDA Forest Service Proceedings RMRS-P-29. 2003. 437

New Initiatives

Both Lyle Laverty and Governor James Geringer have talked about the

various initiatives and directives that are now driving fuel treatment activities.

These initiatives are propelling this program to greater levels than ever before.

These initiatives consist of numerous reports, reviews, directives, and other

agency documents that describe the enormity of the problem and make recommendations for resolution. The most significant initiatives include the 1995

Federal Fire Policy and 2001 Review and Update; multiple General Accounting

Office audits; the National Fire Plan; the combined federal Cohesive Strategy

(Restoring Fire-Adapted Ecosystems on Federal Lands – A Cohesive Strategy

for Protecting People and Sustaining Natural Resources); and the 10-Year

Comprehensive Implementation Plan (A Collaborative Approach for Reducing Wildland Fire Risks to Communities and the Environment, 10-Year

Comprehensive Strategy, Implementation Plan).

Application Focus

Fuel treatments and ecological restoration activities have historically been

focused on remotely based treatments conducted away from population centers. As a result, treatments could be conducted in lower risk situations, with

lower threats to values to be protected. These earlier treatments were conducted on a small-scale basis and achieved only highly localized, stand

maintenance results. These treatments were conducted for resource management objectives, silvicultural support, and hazard fuel reduction around federal

infrastructures and may be representative of the “passive restoration” described

by Greg Aplet in his presentation.

Changing Needs

The ever-increasing complexities of these programs are now trending into

areas where the needs warrant expanding our range of treatment techniques

to fully accomplish objectives. Historical experience may not have given us

the requisite tools and knowledge we need for the demands of today and

tomorrow to keep pace with changing societal values.

In the rush to quickly implement these programs, assign funds, and obtain

results, we have sought quick and easy solutions. A full range of treatment

techniques is available, and no single treatment is best suited for all situations.

Some techniques are not even possible in certain situations. Our fuel treatment experience has been primarily a result of past prescribed burning. Some

limited experience in mechanical treatments has also been gained but is the result of activities completed in support of silvicultural applications and not

specifically designed for fuel treatment or ecological restoration. We need to

utilize the various treatment types at our disposal and maximize the application to the land use situation. Treatment types including chemical, biological,

mechanical, pile burning, mechanical plus pile burning, other multiple treatment combinations, small-scale prescribed fire, landscape-scale prescribed fire,

and wildland fire use are viable options. However, these treatments must be

applied in the correct land use situation. For example, landscape-scale prescribed fire and wildland fire use are not realistic or feasible in the wildland-urban

interface areas while such site-specific treatments as biological, chemical,

mechanical, and pile burning are not economically efficient in wildernesses or

other large undeveloped and inaccessible areas. There are optimal areas of

operation for each treatment type. Windows of opportunity exist and must be

capitalized on. In terms of treatment types across the diverse range of land use

Closing Comments: Fire, Fuel Treatments, and Ecological Restoration—Proper Place, Appropriate Time Zimmerman
438 USDA Forest Service Proceedings RMRS-P-29. 2003.

situations, there is no panacea to the current issues of fuel reduction and management and ecosystem maintenance and restoration.

Accountability and Scrutiny

Now, probably more than ever before, accountability and scrutiny are forefront in the fuel treatment and ecological restoration programs. This is due to

the potential outcomes of fuel treatments, outcomes of lack of treatments,

values to be protected, recent occurrences (including Lowden Ranch, Cerro

Grande, and North Shore of Kenai Lake prescribed fires), and the infusion of

funding. Over the last several decades, land managers have stressed the changes

that were occurring in fuel complexes and ecosystem health. After the 2000

fire season, the President and Congress listened and responded. The National

Fire Plan was formulated and additional funds were provided to federal land

managers to take action on reducing fuel accumulation and protecting ecosystems and communities. This development, though, is a double edged sword.

The acceptance and endorsement of the problem by politicians and Washington level bureaucrats places it in a priority status and provides additional funding.

This is one of the few times that partisan politics has not dominated the funding of strategic land management programs. The fuel treatment and ecosystem restoration program we are now embarking on is enjoying support from eastern and western Governors and Washington politicians, all with a common

goal. We are seeing greater inter-departmental cooperation in the federal land

management agencies, and markedly increased collaboration among federal

and state agencies, tribal representatives, and local groups. However, with

this increased support and funding comes additional scrutiny and attention.

To maintain continued support, accountability and productivity are paramount.

There will always be competition for funds and to maintain and continue to

expand this program, we must make good decisions yet show sustained

accomplishments with programmatic growth. We must reduce the barriers to

implementation and avoid such issues like “analysis paralysis” that Tim Ingalsbee

described in his presentation.

Changing Application Focus

The current need for fuel treatment and ecological restoration is shifting to

include an area that we have not previously given full attention to. This is due,

in part, to the fact that the wildland-urban interface did not always occupy

such a prominent position in wildland areas. In addition, the wildland-urban

interface is generally not on public lands, but on private lands and correspondingly has not been the subject of federal land management programs.

Now, the wildland-urban interface requires significant attention and action.

Our fuel treatment program is moving from one associated with remotely

based treatments only to one having significant applications in both remotely

based and population-based areas. Our fuel treatment program is also taking

on greater levels of risk associated with this changing focus.

The enhanced focus that is being placed on wildland-urban interface area

treatments does not mean a pendulum swing to the point where the goals of

natural resource management and ecosystem maintenance and restoration will

be minimized or ignored. Some very good points have been made during this

conference about wildland-urban interface treatments versus ecological restoration. We have to continue to respond to both sets of objectives and we must fully

understand that fuel treatment is not synonymous with ecosystem restoration.

External scrutiny over the wildland-urban interface is high and stems from

recent General Accounting Office Reports and Testimony, Congressional

Zimmerman Closing Comments: Fire, Fuel Treatments, and Ecological Restoration—Proper Place, Appropriate Time
USDA Forest Service Proceedings RMRS-P-29. 2003. 439

Hearings, budget hearings and language, accountability, and increased reporting requirements. Collaborative delineation and mitigation treatment

development and implementation for wildland-urban interface areas is underway and providing a higher degree of local involvement. Reducing fuels in

wildland-urban interface areas is about creating vegetative complexes that will

burn with less intensity and severity and be less resistant to control. It is about

protecting communities at risk. Wildland-urban interface treatments are being viewed by some as the priority. But, these types of treatments are very

costly and labor intensive. Since the implementation of the National Fire Plan,

wildland-urban interface treatments have required about two-thirds of the

available funding and accounted for about one-third of the total acres treated. You have heard earlier in this conference from Jim Menakis about using

condition classes (fire regime condition classes) as a method to describe cur- rent conditions of vegetation and fuels. These classes depict the degree of

departure from historical fire regimes resulting in alterations of key ecosystem

components such as species composition, structural stage, stand age, and canopy

closure. Wendel Hann provided a description of the joint federal cohesive

strategy for protecting people and sustaining natural resources (“Cohesive

Strategy”) and explained the use of the condition classes to gauge the current

status of ecosystem health across the country and to set long-term strategic

goals for maintenance and restoration. When we look at the proportions of

areas in the various condition classes, we can readily see the ongoing trend.

Ecosystem health and condition is worsening. Without restoration treatments,

the long-term prognosis is for continued worsening of ecosystem health and

continued, if not increased, frequency, intensity, and outcome of wildland

fire. Areas not classified as wildland-urban interfaces are in need of treatments.

Remember during the last one and one-half years, fuel treatments and ecosystem restoration activities in non-wildland-urban interface areas have accounted

for about one-third of expenditures and two-thirds of accomplishments.

How important are the condition class descriptions? A cursory response to

this problem might indicate that we need to treat only the condition class 3

areas, those in the most degraded state. However, we must ask ourselves, Can

we afford to not treat areas in all three condition classes? Greg Aplet of the Wilderness Society presented examples where lower intensity fires burning in

the understory still were responsible for structural losses. Does condition class

matter in the wildland-urban interface?

I submit that all these things are important to the future of this program.

We need to develop a comprehensive fuel treatment and ecological restoration program that is responsive to both protection and restoration objectives.

This program must closely depend on condition class designations, not focus

on only the worst case, but provide for maintenance of the better situations

while restoring the lesser situations to better conditions. It must effect change

in the wildland-urban interface areas regardless of condition classes there. No

matter what the vegetative condition is, the ability to control wildland fires

and minimize burning intensity must be enhanced.

Scheduling

Scheduling affects both the proper place and the appropriate time for fuel

treatment and ecological restoration. Scheduling determines the sequencing

of projects (i.e., away from highest risk areas on continuing maintenance work),

sets priorities, coordinates across jurisdictional boundaries, and influences longterm activities. Scheduling must be an integral part of fuel treatment and

ecological restoration activities to ensure the maximum efficiency and productivity.

Closing Comments: Fire, Fuel Treatments, and Ecological Restoration—Proper Place, Appropriate Time Zimmerman
440 USDA Forest Service Proceedings RMRS-P-29. 2003.

Scale

Scale is critically important. Bill Romme’s presentation provided us with a

solid example of the scale issues that face us and stand-versus-landscape considerations. We can no longer only deal with stand maintenance or site-specific

treatments. We must now expand to landscape-scale fuel treatment applications. However, landscape-scale does not simply infer that larger treatments

must be applied. A program of this scale will include a combination of sitespecific and large-scale treatments using much of the full spectrum of treatment

types to effect significant change over an entire landscape. This type of program, a comprehensive fuel treatment and ecosystem restoration program,

must be developed and implemented. This type of program will be representative of “active restoration” carried out at the ecoregional level as Greg Aplet

described in his presentation.

Information Acquisition

There are many unanswered questions involving fuel treatment and ecological restoration. Some you have heard during the last three days, some have

not been discussed. Examples of the types of questions that have surfaced here

include:

• What are we restoring the system to?

• What are the effects of treatments on fire severity?

• What are reference conditions?

• What are future conditions?

• What are the best restoration parameters?

• How do we treat the wildland-urban interface (you heard Mark Finney

provide new information on this subject)?

• How can decision-making be improved?

Information acquisition is critical to maintaining and improving this program. Management must be dynamic and accept new developments and

incorporate them into actions. The Federal Fire Policy stresses the importance

of incorporating the best available science into fire management. Both Wally

Covington and Greg Aplet reinforced this in their presentations. Wally

Covington very succinctly summarized the importance of information acquisition by stating that without a sound understanding of the ecosystem that we

are dealing with, our decisions will degenerate into ill-informed speculation,

subjective judgment, bias, ideology, and personal policy viewpoints. Jim

Menakis’ and Wendel Hann’s presentations provided examples of science-based

management analyses and support to decision-making.

Collaboration

The future of this program is in collaboration. We must support collaboration at all levels but we must advocate and ensure local collaborative

decision-making. Some examples exist of increased collaboration around the

country but this must become a universal process. Tim Ingalsbee presented an

example of successful local collaboration and how diversity and societal recognition helped implement a difficult project.

Performance Measures

What are measures of performance? These are standards that have developed in response to the new levels of accountability and scrutiny associated

with the program. Performance measures have important value in that they

Zimmerman Closing Comments: Fire, Fuel Treatments, and Ecological Restoration—Proper Place, Appropriate Time
USDA Forest Service Proceedings RMRS-P-29. 2003. 441

identify standards associated with the program that managers need to meet.

Thus, they help to provide structure and framework for program implementation. However, in the haste to implement, we have begun to confuse

performance outputs with performance outcomes. A performance output is

something that occurs from short-term operational activity, such as numbers

of projects proposed, numbers of projects completed, or numbers of acres

treated. A performance outcome, the desired result of the activities, represents what has resulted from long-term programmatic execution, such as

numbers of communities protected, values to be protected with changed fire

protection capability, proportions of condition classes maintained and restored,

numbers of areas influenced or affected from treatments, and sustained incremental increases in programmatic accomplishments.

Trying to describe performance in terms of outputs merely focuses on the

short-term actions. Using performance outcomes provides a better description of long-term influences of programmatic activities and can offer a basis

for program evaluation.

Measuring Success

How do we measure success? To do this effectively, we must review the

goals of fuel treatment and ecological restoration and the program outcomes.

Success cannot be measured in small-scale increments but in establishment

and implementation of a long-term, pro-active restoration-based landscapescale fuel treatment program. This program must be accountable and meet

the scrutiny accompanying increased funding and attention. It must effect

change in the form of increased protection capabilities and reversed ecosystem

degradation. It must enact and utilize collaborative decision-making at all levels. It must be responsive to dynamic ecological situations and not be static in

time. Research has been a fundamental component of such a dynamic program. I have talked about information acquisition, but its importance warrants

repeating. We must learn more, acquire information, and apply this information. Additional topical areas that have not been discussed include:

• What are the interactions with climate change and fire season duration, fire severity, and changing vegetation?

• What is the relevance of historical reference conditions?

Information such as that which Mark Finney presented must be acquired

and applied to produce the most efficient efforts.

Summary

It is certainly an exciting time to be involved in natural resource management. The advent of the National Fire Plan means many things but principally

an effort to resolve a worsening problem in fuel accumulation and ecosystem

health. The National Fire Plan is not the final answer, but a beginning. We

have a long way to go; you know that this situation is the result of a century of

actions based on 100-year-old state-of-the-knowledge. We have learned more

during this time about fire, fuel, and ecosystem dynamics and believe that

charting a new course is necessary. This program of action will not be easily or

quickly achieved. Along the way, there will be much to learn and much to

incorporate into actions.

Closing Comments: Fire, Fuel Treatments, and Ecological Restoration—Proper Place, Appropriate Time Zimmerman
442 USDA Forest Service Proceedings RMRS-P-29. 2003.

Currently, we have three basic questions that have arisen at this conference

to respond to. Information related to these questions has been pervasive to

the various sessions of this conference. These questions are:

• What are we restoring the system to? What about permanently altered

ecosystems? What about not restoring but defining and stabilizing permanently changed ecosystems into new resilient systems?

• Have we created a situation that will allow the program to succeed?

• Will our actions be sufficient to protect communities and ecosystems?

We must answer these questions and evaluate our actions in light of them.

Defendable and supportable responses to these questions will facilitate our

ability to proceed with a successful long-term strategic program of action to

meet the objectives of fuel treatment and ecosystem maintenance and restoration.

I would like to thank all the organizers, speakers, and participants at this

conference; it has been an exciting, worthwhile, and motivational undertaking. I hope everyone here will take the information and enthusiasm back to

your job and channel them into our common goals of fuel treatment and

ecological restoration.

This has been a great conference and I ensure all of you that this will not be

the last one concerning this subject. 

Thank you for this opportunity.

Zimmerman Closing Comments: Fire, Fuel Treatments, and Ecological Restoration—Proper Place, Appropriate Time
USDA Forest Service Proceedings RMRS-P-29. 2003. 443

Poster Abstracts
444 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 445

Poster Abstracts

Fire Severity and Salvage Logging Effects on

Exotics in Ponderosa Pine Dominated Forests

Suzanne Acton, Payette National Forest, New Meadows, ID, and

Forest Sciences Department, Colorado State University, Fort Collins,

CO

The effects of fire severity and salvage logging on the understory vegetation response in ponderosa pine dominated areas of the South Fork Salmon

River Drainage of west-central Idaho were examined five years post-fire. The

study objectives were to correlate the effects of fire severity and salvage logging on exotic species richness and cover; and to examine the effects of scale

in sampling in post-fire understory vegetation. Five years following fire, areas

that burned with greater fire severity had increases in total species cover and

exotic species richness. Low severity fires had lower exotic species richness

compared to high severity and unburned areas. Salvage logging increased exotic species cover, but not exotic species richness. The only result that was

consistent at multiple scales was the relationship between low severity fires

and low exotic species richness. Measurement scale should be an important

consideration when designing future research experiments in this area, with

preference to multiple-scale measurements.

Using the FARSITE Model to Predict the

Differential Effects of Fuel Treatments on

Potential Wildland Fire Behavior in Dry

Forests of the Wenatchee Mountains

James K. Agee and M. Reese Lolley, College of Forest Resources,

University of Washington, Seattle, WA

A pressing issue in fire management is understanding the differences in the

effects of fuel treatments on wildfire behavior. To gain insight into this issue

Fire Area Simulator (FARSITE) was used to model fuel treatment effect on

short-term wildland fire behavior to test differences between mechanical and

prescribed burn fuel treatments. Intensive fuels data were collected as part of

the pre-assessment for the Fire and Fire Surrogates Treatments Study at the

Mission Creek site in the Wenatchee Mountains of Washington. These data

were used to estimate mechanical and prescribed fire treatment effects on

forest fuel profile and loadings using quantitative and qualitative methods.

Finally, FARSITE was used to predict potential short-term wildland surface

and crown fire differences between fuel treatments.
446 USDA Forest Service Proceedings RMRS-P-29. 2003.

Effects of Seasonal Fire on Mortality and

Oleoresin Pressure of Old-Growth Ponderosa Pines

James K. Agee and Daniel D. B. Perrakis, College of Forest Resources,

University of Washington, Seattle, WA

This project examines techniques for evaluating the survivorship of oldgrowth ponderosa pines (Pinus ponderosa Laws.) following restorative

prescribed burning at Crater Lake, OR. The study area was divided into spring

burn units, fall burn units, and control units (no burning). Parameters measured (before and after burning) include tree species and density, understory

vegetation, fuel loading, and ponderosa pine crown vigor. Selected pines will

be intensively monitored following burning by measuring their oleoresin exudation pressure (OEP) using modified pressure gauges. OEP is considered a

reliable indicator of susceptibility to bark beetle attacks, a frequent cause of

mortality following burning. Pre-burn data shows considerable woody fuel

accumulation, limited understory diversity, and fair to poor vigor in overstory

ponderosa pines. It is expected that burning in the spring will result in lower

OEP and higher pine mortality compared to burning in the fall. A threshold OEP

reading that predicts future bark beetle mortality will hopefully be identified.

Consequences of Wildfire on Understory

Vegetation in Untreated Ponderosa Pine Forests

Noah Barstatis, W. H. Moir, Julie Crawford, and Carolyn Hull Sieg,

USDA Forest Service, Rocky Mountain Research Station, Flagstaff, AZ

Heavy fuel loading and high densities of small trees increase the risk for

large stand-replacing wildfires in ponderosa pine forests of the Southwest. In

addition, there is a concern that these high intensity fires will lead to the

spread of a variety of exotic plant species that have been introduced to the

region in recent years. A series of wildfires in 1996 in northern Arizona provided an opportunity for us to examine the understory response through time

following a large, high intensity fire. In 1997 we established permanent plots

on the Hochderffer wildfire and in adjacent unburned sites in northern

Arizona. We sampled plant canopy cover and frequency in a total of 38 plots

each year through 2001. Our preliminary analyses document dramatic differences in the vegetative composition and diversity between burned and unburned

sites, including the presence of multiple exotic species as well as fire-following

native shrubs.

Variation in Fuel Loadings Within Existing

Ponderosa Pine and Mixed Conifer Forests in

the Southern Colorado Front Range

M. A. Battaglia, W. D. Shepperd, and M. J. Platten, USDA Forest

Service, Rocky Mountain Research Station, Fort Collins, CO

Assessment of the quantity and type of fuels present in a forest is important

for the proper application of fire behavior model predictions. The objective of

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 447

this study was to obtain quantitative data on fuel loadings that exist under

ponderosa pine and mixed conifer forest types typical of the southern Colorado

Front Range. Fuels inventory and stocking data were collected during 2001

from 479 plots on the Manitou Experimental Forest (MEF) in south central

Colorado. The plots studied contained a variety of stocking and physiographic

conditions and these relationships were explored to determine whether any

correlations existed between fuel load and community condition. This poster

describes the distribution of fuel loadings and explores relationships between aspect, elevation, stocking, species composition and fuel characteristics.

Forest Health and Tree Removal Equipment in

the Wildland-Urban Interface—An Educational

Video

David R. Betters and Robert Avera, Department of Forest Sciences,

Colorado State University, Fort Collins, CO

Forest health is a key issue in the wildland-urban interface. Operational

programs to improve forest health conditions in the interface typically involve

thinning, or tree removals, of smaller diameter trees on privately owned lands.

Implementing these programs requires 1) persuading private landowners,

through education, to adapt and implement tree thinning operations on their

properties, and 2) using tree removal equipment that is more cost effective for

small diameter tree harvesting and suited for smaller acreages characteristic of

private interface land holdings. The objective of this project is to develop a

video and an accompanying information packet that addresses the two needs

mentioned above. The video/packet will be created, critiqued via a survey,

and then finalized. The video/packet will be applied in two wildland-urban

interface community case studies concerning the adoption of fire mitigation

programs. The finalized video/packet will be made available through

Colorado State University Cooperative Extension.

A Community-Level Process for Adoption of

Forest Restoration and Fire Mitigation Programs

David R. Betters and Christy L. Higgason, Department of Forest

Sciences, Colorado State University, Fort Collins, CO

The National Fire Plan and its emphasis on restoring health to fire-adapted

ecosystems and reducing community risk of wildland fires has created an overwhelming need for collaboration at all levels in Colorado. The nature of this

problem requires the involvement not only of a variety of local organizations

and citizens but also the coordination and participation of a number of resource agencies having separate but somewhat overlapping roles regarding

communities and natural resource sustainability and forest health. To facilitate

community involvement and reduce confusion in a situation where several

agencies offer public service through land stewardship, a process for local community-level decision-making is key. The objective of this study is to develop

a process coordinated amongst agencies in Colorado that facilitates community/interest group agreement for adopting and implementing a forest

restoration/fire mitigation strategy. The analysis of information collected in

Poster Abstracts
448 USDA Forest Service Proceedings RMRS-P-29. 2003.

the study includes two separate reviews of process. The first review involves

community decision-making processes in general. This entails generalized process and communication theory applicable regardless of problem area. The

second review involves existing processes used in fire mitigation in the wildland-urban interface. This information includes an overview of current processes

used in fire mitigation programs in western states that include educational

packages. These processes and education messages are examined to determine

if consistency exists and whether improvement can be made overall by providing the best delivery content and means. The reviews are used to propose an

overall process to facilitate adoption of community fire mitigation programs.

It is envisioned that the information gathered from this project will also serve

as a supplement to related land management planning efforts.

Response of Understory to Thinning and

Prescribed Burning in Northeastern California

Melissa Borsting, Division of Ecosystem Sciences, College of Forest

Resources, University of Washington, Seattle, WA

My research explores the short-term response of understory vegetation to

silvicultural and fuel reduction treatments in ponderosa pine forests of northern California (Blacks Mountain Experimental Forest). It is important to

understand these responses, as understory vegetation contributes significantly

to biological diversity, nutrient cycling, wildlife habitat, and fire regime in

these forests. The experimental design consists of two thinning treatments

(high and low structural diversity), with and without prescribed burning, replicated at each of three locations. In this poster, I discuss the short-term

responses of the dominant understory species and growth forms (e.g., annuals

vs. perennials, herbs, graminoids, and shrubs). I compared changes in plant cover,

species richness, and community composition, and address treatment-level responses, within-treatment heterogeneity, and relationships with burn intensity.

The Effects of Herbivory on Quaking Aspen

(Populus tremuloides) Regeneration Following

Various Treatments in South-Central Utah

Shauna-Rae Brown, Arizona State University, Chandler, AZ

Aspen (Populus tremuloides) ecosystems are known for their high biodiversity

and are believed to have been a dominant type in the Intermountain West

since the early Pleistocene. However, aspen ecosystems have dramatically declined since the 1870s. Over the past eight years, Fishlake National Forest,

Utah has been working to stem the tide of aspen decline by treating for aspen

restoration. During the summer of 2001 (June - October), 34 sites were surveyed to monitor the success of aspen regeneration following a variety of

treatments including wildland fire, clearcutting for aspen regeneration, and

clearcutting followed by prescribed fire. All clearcut sites that were monitored

had some type of fencing erected to serve as study controls. Sites fenced to

preclude all cattle and wildlife herbivory produced the highest number of aspen suckers compared to adjacent unfenced or cattle-excluded sites. Of the

thirty-four sites surveyed, only one had no measurable aspen regeneration,

which was due to high ungulate utilization.

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 449

Cerro Grande Fire Restoration at Los Alamos

National Laboratory

Kevin Buckley, Sam Loftin, Jeff Walterscheid, and Mike Alexander,

Los Alamos National Laboratory, Los Alamos, NM

The Cerro Grande Fire of 2000 had a huge impact on forests on and around

Los Alamos National Laboratory (LANL). Immediately after the fire, there

were concerns about increased erosion and flooding and its impacts on contaminated soil. An Emergency Rehabilitation Project was created to evaluate

and estimate the impacts of the Cerro Grande Fire on LANL property, design

appropriate methods to mitigate the effects of increased erosion and storm

water runoff, and implement these measures to prevent further damage to

people, property, and the environment. Seeding, raking, contour felling, log

erosion barriers, straw wattles, straw mulch, and hydromulching were among

the treatments used to stabilize soils and retain water on approximately 648

ha (1,600 acres) of moderately and severely burned hillslopes at LANL. Assessments show a highly successful response from combinations of seeding,

raking, and mulching.

Mechanical Fuels Reduction Contract Underway

at Richfield Bureau of Land Management

Linda Chappell, Doug Thurman, and Russell Ivie, Richfield Field

Office, Utah Bureau of Land Management, Richfield, UT

Continuous pinyon-juniper fuels on the south, east, and north sides of

Mayfield in south-central Utah are a potential wildfire threat. The nearby previously chained lands administered by the Bureau of Land Management were

divided into three separate units to treat. This was in an effort to allow bidders

of any size an opportunity to obtain the contract. Twenty-one hundred acres

of pinyon/juniper fuel are now contracted for cutting in 2001-2002. A copy

of the contract will be available. Following the treatments some broadcast

burning will occur on areas with heavier fuel loads. Pre- and post-cut photos

are included.

Definition of a Fire Behavior Model Evaluation

Protocol: A Case Study Application to Crown

Fire Behavior Models

Miguel G. Cruz, School of Forestry, University of Montana,

Missoula, MT; Martin E. Alexander, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada; and Ronald H.

Wakimoto, School of Forestry, University of Montana, Missoula, MT

Testing and evaluation of models is an important and fundamental component of the scientific method, leading to model understanding and the increase

of their credibility. The process of model evaluation has been seen differently

by several authors due to the philosophical interpretations of what a model is.

An important aspect when considering model evaluation is the

Poster Abstracts
450 USDA Forest Service Proceedings RMRS-P-29. 2003.

definition of the criteria that should be followed, which depends on the type

of model being evaluated and its potential application. When considering fire

behavior science, theoretical models developed to understand certain physical

and chemical phenomena should be evaluated in a different form than operational models built to support decision-making. In the present paper a model

evaluation protocol is proposed to encompass different aspects, such as: (1)

model conceptual validity, (2) data requirements for model validation, (3)

sensitivity analysis, (4) inter-model comparison, (5) predictive validation, and

(6) statistical tests. The proposed protocol was applied to evaluate fire behavior models aimed at predicting crown fire initiation and spread with potential

application in fire management decision support systems. The evaluation protocol highlighted the limitations of the discussed models and the implications

of such limitations when applying those models to support fire management

decision-making. The particular case of definition of fuel treatments aimed at

reducing crown fire hazard was analyzed. The model limitations identified

through the results reveal deficiencies in the state of knowledge of determinant processes in crown fire behavior and point to pertinent research needs.

Monitoring Fuel Conditions and Predicted Fire

Behavior Before and After Varied Fuel

Treatments in the Quincy Library Group Area:

Site and Landscape Scale Findings and

Implications

Jo Ann Fites-Kaufman, USDA Forest Service, Adaptive Management

Services Enterprise Team; Bernie Bahro, USDA Forest Service,

Pacific Southwest Region; and Larry Hood, USDA Forest Service,

Lassen National Forest, CA

A forest health pilot including mechanical treatments (thinning and biomass) and prescribed burning was conducted on portions of three National

Forests. We monitored site and landscape scales of fuel conditions. The 85

pilot projects encompassed over 45,000 acres and 993 individual treatment

units. All projects were mapped digitally and detailed data were collected on

surface, understory, and crown fuels. Vegetation was collected for random

units. We evaluated several statistical approaches given constraints of nonrandom treatment assignments and lack of before and after data. Randomization

tests and Impact vs Reference Sites analyses were conducted. Key findings

were that sites selected for burning had lower canopy covers, tree densities,

and crown bulk than those selected for mechanical fuel treatments. Regardless

of treatment, many plots were predicted to have crown fire at high fire weather

conditions. At the landscape scale, the pilot resulted in treatment of less than

10 percent of the landscape.

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 451

Applying and Evaluating Ecosystem

Reinvestment A Case Study of the Bitterroot

Watershed, Montana

Stephanie Gripne, Crockett Wildlife Conservation Program, School

of Forestry, University of Montana, Missoula, MT

Ecosystem reinvestment is one of the emerging frameworks associated with

ecosystem management. Ecosystem reinvestment describes any activity that

increases the value of ecosystems and communities by directing revenues from

natural resource activities toward efforts to restore and maintain these systems. Ecosystem reinvestment builds off the premise that ecosystem goods

and services can be thought of as sources of capital. Natural and social capital,

just like physical capital, can degrade over time and needs investments of resources in order to maintain their value/function. My research objectives are

to apply the ecosystem reinvestment framework to the Bitterroot Watershed

ecosystem and evaluate the potential of this framework to actually increase the

level of resources that are reinvested back into the ecosystem. Specifically, I

propose to determine 1) the market and nonmarket economic value of several

ecosystem goods and services generated in the Bitterroot Watershed, 2) the

cost of restoring different scenarios of ecosystem goods and services, 3) the

individuals who benefit from these ecosystem goods and services, and 4) the

available mechanisms for providing the support needed to maintain these forest ecosystems using a economic analyses, a contingent valuation

willingness-to-pay survey, stakeholder analysis, and literature review. My proposed study will provide one of the first formal evaluations of the application

of ecosystem reinvestment as a model for maintaining and restoring ecosystems. Additionally, this study will improve the state of knowledge about market

and nonmarket economic values of forest ecosystem goods and services in the

Bitterroot Watershed, which to date, has not been comprehensively been explored.

Demonstration Plots for Comparing Fuel

Complexes and Profile Development in

Untreated Stands Vs. Stands Treated for the

Management of Spruce Beetle Outbreaks

Elizabeth Hebertson, USDA Forest Service, Ogden Field Office,

Ogden, UT; Michael Jenkins, Department of Forest Resources, Utah

State University, Logan, UT; and Linda Wadleigh, USDA Forest

Service, Williams, AZ

The activity of insects, diseases, and abiotic agents is known to contribute

to changes in the characteristics of fuels complexes and associated fire behavior. Landscape-scale density management strategies have been proposed as

viable alternatives to sanitation or salvage for managing insect and disease

outbreaks. The effect of various density treatments on fuels complexes, or

fuels development, however, is not known. An important agent of disturbance

in Intermountain Region National Forests of the United States is the spruce

beetle. Outbreaks have caused extensive mortality resulting in significant loss

Poster Abstracts
452 USDA Forest Service Proceedings RMRS-P-29. 2003.

of timber, recreational opportunities, and aesthetics. Mortality

resulting from outbreaks has also resulted in increased dead fuel loads and will

likely alter the fuels complex of infested stands over time. The Fishlake

National Forest in cooperation with the Utah State Division of Forestry, Fire

and State Lands will implement two density management treatments in several spruce-fir forests with the purpose of reducing susceptibility of these stands

to spruce beetle infestation while enhancing tree vigor. This situation provides an excellent opportunity to establish permanent demonstration plots in

treated versus untreated stands. Fuels inventories and appraisals will be conducted to determine changes in the fuel complex and profile over time. Based

on the fuel inventory and appraisal data fuels treatment strategies including a

combination of mechanical and biomass, utilization will be implemented on

the demonstration plots. Prescribed fire will also be considered for treating

fuel provided all prescription variables are met. Other benefits derived from

this information include the demonstration of strategies for managing insect

outbreaks and fuel development and manipulation.

The Effects of Long-Term Repeated Prescribed

Burning on Nutrient Regimes in Loblolly Pine

Stands in the Gulf Coastal Plain in Arkansas

Jennifer J. Henry and Hal O. Liechty, School of Forest Resources,

University of Arkansas, Monticello, AR; and Michael S. Shelton,

USDA Forest Service, Southern Research Station, Monticello, AR

Nutrient levels of various forest components were measured in loblolly pine

stands within the Gulf Coastal Plain of Arkansas. The stands evaluated had

either been burned every 2-3 years for the past 20 years or had not received

any prescribed burning. Comparisons were made between stand types (burned

and unburned) to assess the long-term effects of repeated prescribed fire on

nutrient regimes. Due to low fuel loadings and wet/cool weather conditions,

fires were of low to moderate intensity. Pine litterfall collected in the burned

stands was higher in K, Ca, and Mg but lower in C than in the unburned

stands. Although nutrient levels of the forest floor, live foliage, and the soil

did not generally differ between stand types, N concentrations were lower in

the understory vegetation of the burned compared to unburned stands. Additional research is being conducted to determine the nutrient dynamics from

the latest prescribed fire.

The Citizen’s Call for Ecological Forest

Restoration: Forest Restoration Principles and

Criteria

Timothy Ingalsbee, Western Fire Ecology Center, Eugene, OR

In February 2001, a diverse group of conservationist and community forestry groups from around the country held a Restoration Summit in Boulder,

Colorado to develop scientifically sound and socially progressive restoration

principles. Their goal was to create a document to help grassroots conservationists and forest practitioners advocate for Congressionally funded restoration

programs, and evaluate federal restoration policies and projects. The Boulder

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 453

Summit initiated a year-long process resulting in a consensus-based

document called “The Citizen’s Call for Ecological Forest Restoration: Forest

Restoration Principles and Criteria.” This document and the growing coalition of organizations and individuals who endorse it and plan to utilize it have

the potential to affect forest restoration programs, policies, and practices on

federal lands in the near future. This poster presents some of the core principles concerning ecological restoration, ecological economics, and community

and workforce development, with a special focus on fire restoration and fuels

treatments.

A Decision Support System for Spatial Analysis

of Fuel Treatment Options and Effects at

Landscape Scales

J. Greg Jones and Jimmie D. Chew, USDA Forest Service, Rocky

Mountain Research Station, Missoula, MT

We present an approach for analyzing fuel treatment activities both spatially

and temporally at the landscape scale that employs strengths of both simulation and optimization modeling. SIMulating Vegetative Patterns and Processes

at Landscape ScaLEs (SIMPPLLE), a stochastic simulation modeling system,

is used for projecting vegetative change in the presence of interacting natural

processes, with or without management treatments. The Multi-resource Analysis and Geographic Information System (MAGIS), an optimization modeling

system, integrates biophysical and socioeconomic information and schedules

management practices spatially and temporally. The combination of SIMPPLLE

and MAGIS provides a powerful analytical methodology for: 1) analyzing the

extent and likely location of insects, disease, and fire both in the presence and

absence of treatments, 2) developing spatial and temporal treatment alternatives for addressing fuels treatment along with other resource objectives, and

3) evaluating those alternatives in a manner that captures the combined effects of treatments and disturbance processes.

Invasive Plants and Wildfire on the Cerro

Grande Fire, Los Alamos: Integration of Spatial

Information and Spatial Statistics

Mohammed A. Kalkhan, Natural Resource Ecology Laboratory;

Philip N. Omi and Erik J. Martinson, Department of Forest Sciences;

Thomas J. Stohlgren and Geneva W. Chong, Natural Resource

Ecology Laboratory; and Molly A. Hunter, Department of Forest

Sciences, Colorado State University, Fort Collins, CO

The integration of spatial information and spatial statistical modeling can

be used to investigate the spatial relationships among fuels, wildfire severity,

and post-fire invasion by exotic plant species through linkage of multi-phase

sampling design and multi-scale nested sampling field plots, and pre- and postfire. This technique provides useful information and tools for natural resource

managers, especially in describing landscape-scale fire regimes, invasive plants,

and ecological and environmental characteristics for the Cerro Grande fire

site, Los Alamos, NM, USA. We integrated filed data and spatial information

Poster Abstracts
454 USDA Forest Service Proceedings RMRS-P-29. 2003.

(Landsat TM Data, transform TM Data, and topographic data) with spatial

statistics for modeling large-scale and small-scale variability to predict the distribution, presence, and patterns of native and exotic species. All models were

selected based on lowest values of standard errors, AICC statistics, and high R2

.

Guidelines for Restoring an Unlogged Ponderosa

Pine/Douglas-Fir Landscape in the Colorado

Front Range After a Century of Fire Suppression

Merrill R. Kaufmann , Paula J. Fornwalt, Laurie S. Huckaby, and

Jason M. Stoker, USDA Forest Service, Rocky Mountain Research

Station, Fort Collins, CO; and William R. Romme, Department of

Forest Sciences, Colorado State University, Fort Collins, CO

Current forests in the unlogged ponderosa pine/Douglas-fir landscape at

Cheesman Lake in the Colorado Front Range are denser with fewer and smaller

openings and more young Douglas-fir trees than occurred historically, largely

the result of fire suppression. We estimate from spatial maps and temporal

patterns of historical fires that four significant fires would have occurred since

the last major fire in 1880. The ages of old trees were used to estimate the

severity of past fires. Fire and tree age data were used to estimate plausible

locations and severities of excluded 20th century fires. Landscape structure for

1900 was estimated by “de-growing” forest polygons derived from recent

aerial photos and field plot data. FVS was used to estimate landscape structure

during the 20th century with the simulated effects of 20th century fires. A

frame model depicts changes in the structural components of the landscape

from around 1500 to the present, and compares the effects of fire suppression

with a plausible 20th century natural fire scenario.

A Device and Method for Conducting Small-Plot

Experimental Burn Treatments

John L. Korfmacher, Jeanne C. Chambers, and Robin J. Tausch,

USDA Forest Service, Rocky Mountain Research Station, Forestry

Sciences Laboratory, Reno, NV

Large prescribed burns are valuable tools in assessing fire effects on soils

and vegetation, but administrative and safety concerns may require smallerscale efforts. We constructed a 3.4 m diameter fire enclosure for conducting

experimental burn treatments on small (~10 m2

) circular plots, using sheetmetal,

electrical conduit, and other commonly available materials. We field tested the

enclosure in a sagebrush-grass ecosystem in central Nevada, and evaluated

peak fire temperatures using small metal tags striped with temperature sensitive paint. Under-shrub microsites averaged 381∞C, significantly hotter than

under-grass (307∞C) and bare-ground (310∞C) microsites. Subsurface (2 cm

depth) temperatures rarely exceeded 79∞C, the lowest temperature detectable

by our method. The fire enclosure contained the fire and did not permit escape of embers or firebrands. The fire enclosure, burn technique, and

temperature monitoring method we use here are inexpensive, easily deployed,

and desirable for experiments where larger-scale burns are impractical.

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 455

Direct Fire Effects Following the Jasper Fire,

Black Hills National Forest, SD

Leigh Lentile and Skip Smith, Department of Forest Sciences,

Colorado State University, Fort Collins, CO; Wayne Shepperd,

USDA Forest Service, Rocky Mountain Research Station, Fort

Collins, CO; and Tricia Balluff, Department of Forest Sciences,

Colorado State University, Fort Collins, CO

In late August 2000, the Jasper Fire burned 33,000 ha of the Black Hills in

South Dakota. The Jasper Fire burned under a variety of fuel and weather

conditions creating a mosaic pattern of tree mortality within patches of varying size and extent. Remotely sensed canopy reflectance indicates that 39, 32,

and 24 percent of the fire burned under high, moderate, and low severity

conditions. Within the context of a larger monitoring design, we will provide

an initial characterization of severity by quantifying direct fire effects on the

overstory and the forest floor. An understanding of why particular trees die

and what we can expect in terms of recovery based on different fire effects

may be useful in planning.

We observed an increase in the proportion of crown, bole, and forest floor

affected by fire along a severity gradient from low to high. In low and moderate severity treatments, 21-70 percent of the crown was scorched. In high

severity treatments, 92 percent of the crown was consumed. We indexed fire

severity as the product of the proportion of the ground area charred and the

severity of char scaled from low (1) to high (3). Fire severity was 115 for low

and 236 for high treatments on a scale from 100-300. Forest floor litter depths

decreased from 2.54 cm in unburned stands to 0.15 cm in high severity treatments. Litter mass decreased from 1265 g m-2 in unburned areas to 82 g m-2

in high intensity treatments.

Tree mortality due to direct fire effects increased from low to high fire

severity. Fire selectively killed smaller trees in low and moderate severity treatments. However, no trees, independent of diameter, survived in high severity

treatments. This observation suggests that a threshold may exist between diameter and fire survivability in low and moderate severity fires in ponderosa

pine systems in the Black Hills.

Experimental Approach for Monitoring the

Jasper Fire, Black Hills National Forest, SD

Leigh Lentile and Skip Smith, Department of Forest Sciences,

Colorado State University, Fort Collins, CO; Wayne Shepperd,

Anna Schoettle, José Negrón, and Kevin Williams, USDA Forest

Service, Rocky Mountain Research Station, Fort Collins, CO; and

Tricia Balluff, Department of Forest Sciences, Colorado State

University, Fort Collins, CO

In late August 2000, the Jasper Fire burned ~33,000 ha or 7% of the Black

Hills National Forest, South Dakota. This was the largest recorded fire in the

Black Hills. The Jasper Fire burned under a variety of vegetative, topographic,

Poster Abstracts
456 USDA Forest Service Proceedings RMRS-P-29. 2003.

and meteorological conditions creating a mosaic of vegetative mortality in

patches of varying size and extent. This fire appears to be similar to other

historical fires in interior forests of the Black Hills in that it was an extensive,

late season burn. The heterogeneity of fire effects suggests that fire behavior

was spatially and temporally variable, exhibiting characteristics of surface, crown,

and a combination of these fire types.

As part of a five-year effort, we established a network of permanent sites

within the burn to monitor the influence of pre-fire vegetative conditions, fire

size, and consumptive patterns on post-fire ecological succession. Direct fire

effects on overstory, understory, forest floor, and soil components were assessed in 2001. Measurements of direct fire effects on soil and vegetation will

be translated to quantitatively-based severity indices. Given the spatial extent

and heterogeneity of the burn we can address questions relating to fire behavior and the timing and response of vegetation recovery. An assessment of mixed

fire behavior within the signature of the Jasper Fire may provide a more unified and plausible idea of common fire behavior that has shaped landscapes

within the Black Hills ecosystem.

Non-Structural Value Added Application of

SDU Materials in Landscape Architecture

Applications

Kurt H. Mackes and David G. Buech, Department of Forest Sciences,

Colorado State University, Fort Collins, CO

Most materials culled from densely packed, even-aged stands are smalldiameter trees (4- to 8-in. stems). Material of this size is difficult to process

and extract economic value from. Most of this material is limited to nontraditional markets because of lack of grading standards/rules and reliable

mechanical properties. There is demand for markets that require minimal material processing, are local, do not require grading and can be used without

building code approval. This paper outlines applications in Landscape Architecture and the opportunities that they present. Applications include: fencing,

retaining walls, walkways, nonstructural decks, and other “garden structures.”

Wood Utilization and Marketing Strategy for

Small Diameter Wood in Boulder County

Kurt H. Mackes and Julie E. Ward, Department of Forest Sciences,

Colorado State University, Fort Collins, CO; and Craig Jones,

Winiger Ridge Ecosystem Management Pilot Project, Colorado

State Forest Service, Boulder District

The Winiger Ridge Ecosystem Management Pilot Project in Boulder County,

Colorado is one of 28 demonstration sites that are part of an innovative land

management initiative between public agencies and landowners to

collaboratively manage resources to improve overall conditions of the ecosystem. The purpose of this research is to develop a marketing strategy for utilizing

the characteristically small diameter wood (<12" d.b.h.) that would be removed from this project and other fuel hazard reduction projects in Boulder

County. This will be accomplished by characterizing the resource being

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 457

harvested (quality, quantity, availability, etc.), identifying existing markets for

materials, and exploring the potential for new markets that could utilize this

resource.

Research on Stand Management Options for

Reducing Fuels and Restoring Two-Aged

Lodgepole Pine Communities on the

Tenderfoot Creek Experimental Forest

Ward McCaughey, Forestry Sciences Lab, USDA Forest Service,

Rocky Mountain Research Station, Bozeman, MT

Fire-dependent lodgepole pine stands comprise significant acreages of midand upper-elevation forests in the Northern Rockies, providing wood products, wildlife habitat, livestock forage, water, recreational opportunities, and

expansive viewsheds. Many lodgepole pine stands are in late-successional stages

and at risk to pests and catastrophic-scale fires. Tenderfoot Creek Experimental Forest is located on the Lewis and Clark National Forest in the Little Belt

Mountains of Central Montana. Twenty percent of the lodgepole pine stands

on the experimental forest were found to be two-aged and another 30 percent

were in an indistinct mosaic of a duel-fire complex. This paper describes preliminary results of the Tenderfoot Research Project designed to evaluate

two-aged harvest methods in lodgepole pine stands by integrating silviculture

and prescribed fire. Research studies evaluate the effects of harvesting and

prescribed fire on several resources such as water quality and quantity, wildlife,

forest fuels, and vegetation response.

The Role of Wildland Fire and Subsequent

Insect Attack on Ponderosa Pine Mortality

Joel McMillin, Linda Wadleigh, and Carolyn Hull Sieg, USDA Forest

Service, Rocky Mountain Research Station, Flagstaff, AZ; José

Negrón, USDA Forest Service, Rocky Mountain Research Station,

Fort Collins, CO; Ken Gibson, USDA Forest Service, Region 1; Kurt

Allen, USDA Forest Service, Region 2; and John Anhold, USDA

Forest Service, Rocky Mountain Research Station, Flagstaff, AZ

The unprecedented fire year of 2000 provided an opportunity to quantify

cumulative impacts of wildland fires and subsequent insect attack on ponderosa pine mortality over a large region. In 2001 we established plots in four

National Forests: Black Hills in South Dakota, Custer in Montana, ArapahoRoosevelt in Colorado, and Coconino in Arizona. In each area, we sampled

1500+ trees in burned areas and 500 trees in unburned areas. For each tree,

we measured height, dbh, pre-fire live crown ratio, percent crown scorch,

percent crown consumption, percent scorched basal circumference, scorch

height on the bole, and insect presence. In addition, we collected four

phloem samples from each of 200+ additional trees in each area to quantify

the relationship between exterior signs of fire-caused damage and cambium

damage. Tree mortality will be monitored for three years post burn. Our goal

is to provide land managers with quantitatively based guidelines for assessing

potential tree mortality following wildland burns.

Poster Abstracts
458 USDA Forest Service Proceedings RMRS-P-29. 2003.

Roundheaded Pine Beetle, Dendroctonus

adjunctus, and Fuel Loads in the Sacramento

Mountains, New Mexico

José F. Negrón and John Popp, USDA Forest Service, Rocky

Mountain Research Station, Fort Collins, CO

The roundheaded pine beetle (RPB), Dendroctonus adjunctus, is one of the

most important bark beetles associated with ponderosa pine, Pinus ponderosa,

in the Southwest. Outbreaks of this insect have caused extensive mortality in

the Lincoln National Forest, NM, in the 1970s and in the 1990s. During the

mid-1990s a network of plots was established in infested and uninfested stands

to develop models to estimate the probability of infestation by the RPB. During remeasurement of these plots during the summer of 2001, we quantified

fuel loads associated with tree mortality caused by the RPB. We used Brown’s

method to inventory coarse woody debris (cwd) in the plots. Transects were

placed in a randomly selected direction but with the center of the transect

coinciding with the center of our plots. We observed no differences in cwd in

the 0 – 1

⁄4 inch or the 1⁄4 to 1.0 inch size classes between the infested and the

uninfested stands. We observed significantly higher cwd debris levels in the

1.0 to 3.0 inch class; in the >3.0 inch sound and rotten size classes; and in

total cwd in the infested stands. We used BehavePlus (USDA Forest Service)

to simulate spread and flame length using default values for the timber with

litter and understory fuel model but adjusting the 1-, 10-, and 100-hour fuels

to the levels we observed. We obtained increases in flame length and rate of

spread with corresponding increases in 100-hr fuels. The largest increases in

fuel loads are associated with cwd >3.0 inches. These increases will likely result in increased fire severity. These findings suggest that bark beetle outbreaks

can cause significant increases in fuel loads, influence fire behavior, and perhaps increase the severity of the fires. These findings can have profound

implications for managing dead trees resulting from extensive bark beetle outbreaks.

Historic Fire-Vegetation Relationships and the

“Reference Conditions” Concept

Steve Norman, USDA Forest Service, Redwoods Sciences Lab,

Arcata, CA; and Alan H. Taylor, The Pennsylvania State University,

University Park, PA

In the Interior West, historic stand densities and disturbance histories have

been reconstructed to describe how vegetation has changed due to a century

or more of livestock grazing, logging, and fire suppression. We provide reconstructions for the ponderosa pine/Jeffrey pine forests of Lassen National Forest,

California, using tree ring-based analyses from multiple half-hectare plots. We

show that pre-Euroamerican fire regimes and vegetation dynamics varied both

spatially and temporally. Changes in fire intervals, season, and extent varied among

sites in part from the destabilizing effects of climate. Tree establishment was

often episodic and only somewhat consistent with changes in disturbance.

These results suggest that reference conditions, as defined by either reconstructed vegetation structure or fire intervals, may be contingent on both the

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 459

time period and location considered. In complex landscapes, such landscape

heterogeneity may limit the application of the reference condition concept for

fire and fuel reduction strategies.

Sustainability of Thinning and Prescribed Fire

Programs to Improve Forest Condition Along

the Front Range of Colorado

Philip N. Omi, Antony S. Cheng, Douglas B. Rideout, Erik J.

Martinson; and Gabriel D. Chapin, Department of Forest Sciences,

Colorado State University, Fort Collins, CO

Fire suppression initiatives of the 20th Century have contributed to unprecedented fuel loadings and subsequent wildfire severities. The fires of 2000 will

long be remembered for their social, economic, and ecological impacts, as

well as the $1.8 billion presidential initiative that resulted. While this initiative

is well-intended, many information gaps exist relative to the acceptability of

the expanded thinning and prescribed burning programs included under this

initiative to improve forest condition in Colorado. The two primary objectives of the proposed project are to (1) conduct an interdisciplinary examination

(hypothesis tests) of the sustainability of expanded thinning and burning programs to improve forest condition along the Front Range of Colorado and

(2) conduct a series of collaborative workshops involving professional land

managers and affected publics, based on knowledge gained from hypothesis

tests. Four primary information sources (i.e., agency records, field sampling,

survey, focus groups) will be used to test a series of hypotheses related to the

social, economic, and ecological concerns associated with the proposed thinning and burning programs. The collaborative workshops will also be used to

evaluate additional hypotheses. This project will attempt to fill the information voids that exist by developing social, economic, and ecological criteria

that can be used to evaluate the sustainability of expanded thinning and burning programs. The collaborative workshops, to be held during the final stages

of the study, will use knowledge gained during the study to involve professional land managers and affected publics. The project will provide information

and criteria that could be used by county faculty to develop forest restoration

programs that are socially, economically, and ecologically sustainable. The results will be relevant to all current and proposed programs that involve

mechanical thinning and/or prescribed fire treatments.

Using RX Fire to Maintain Shaded Fuelbreak

Areas in Southern Spain

Philip N. Omi and Francisco Senra, Department of Forest Sciences,

Colorado State University, Fort Collins, CO

The use of broadcast burning as fuel treatments is not used in Southern

Spain nowadays. This is especially due to the characteristics of the vegetation

in these Mediterranean areas, such as an absence of fine fuels or existence of

multiple strata. However, the use of prescribed fire could be an interesting

alternative for the maintenance of shaded fuelbreak areas. With the aim of

studying the viability of such fuel treatment, eight shaded fuelbreak areas were

Poster Abstracts
460 USDA Forest Service Proceedings RMRS-P-29. 2003.

burned in Malaga province. All of them were Pinus pinaster stands, but with

different understory structures and species, and using different firing techniques. The objectives were specially focused on comparing fuel consumption

among the plots and defining the minimum base crown height to secure the

survival of the trees as well as maintain the burn within prescription. The

results will ease the use of RX-fire as a fuel treatment in forest fire prevention

in Southern Spain.

Cerro Grande Rehabilitation Project

Robert Paul, Laura Paul, Lindsey Quam, Shannon Smith, Lisa

Dunlop, Stephen Mee; and Victoria George, Los Alamos National

Laboratory, Los Alamos, NM

Following the Cerro Grande Fire of May 2000, the Facility and Waste

Operations at Los Alamos National Laboratory (LANL) received emergency

funding to develop and support the Cerro Grande Rehabilitation Project. The

project is responsible for landscape level mitigation of catastrophic wildfires

on 10,000 acres of LANL property. A LANL moratorium on prescribed burning exists, so thinning utilizing site-specific defensible space, fuel break, and

general thinning prescriptions are being used to reduce tree density and mitigate fuels. In general, prescriptions are based on prior piñon-juniper restoration

and fuel mitigation research and on historical ponderosa pine density and expected fire behavior. Site-specific fuel mitigation implementation plans were

developed that protect threatened and endangered species, the wildland urban interface, forest health and sustainability, streamside management zones,

soil, air quality, cultural sites, utility corridors, and public safety. The goals of

the site-specific implementation plans include the creation of defensible space

and fuel breaks around structures, ecologically sound slash disposal and fuel

reduction methods, utilization of equipment and performing operations at

the appropriate time and place, and maintaining or improving aesthetic quality. Desired future conditions include a reduction of the risk of catastrophic

wildfire and its associated effects on ecosystem health and the urban interface

to a politically and socially acceptable level, improved and maintained forest

health and wildlife habitat, and support of fuel mitigation efforts across the

southwest. Management tools presently in development include building a

post-treatment continuous forest inventory and a comprehensive modeling

package to guide adaptive management and maintenance of desired future conditions. The project is in its second year and scheduled for completion in 2003.

Mapping Fuels and Fire Regimes Using Remote

Sensing, Ecosystem Simulation, and Gradient

Modeling

Matthew G. Rollins, Robert E. Keane, and Russell A. Parsons, USDA

Forest Service, Rocky Mountain Research Station, Fire Sciences

Laboratory, Missoula, MT

Maps of fuels and fire regimes are essential for understanding relationships

between fire and landscapes and for enlightened restoration of fire regimes to

within historical ranges. We present an approach to fuels and fire regime mapping using gradient-based sampling, remote sensing, ecosystem simulation,

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 461

and statistical modeling to create maps of fuels and fire regimes. We developed a database including 40 variables describing indirect, direct, resource,

and functional gradients of physiography, spectral characteristics, weather, and

biogeochemical cycles for a 5,830-km2 area in Northwestern Montana. Using

a variety of statistical techniques we created maps of fuel loads, fuel model, fire

interval, and fire severity with independently assessed accuracies from 50 - 80

percent. Incorporating direct, resource, and functional gradients significantly

improved map accuracy over maps based purely on indirect gradients. These

maps provide information to assess the hazards and risks of fire when deciding

how best to restore forests to within historical ranges of variation.

Role of Fire in the Future of White Pine

Populations in Colorado

A.W. Schoettle , USDA Forest Service, Rocky Mountain Research

Station, Fort Collins, CO; and C.M. Richards, National Center for

Genetic Resources Preservation, Fort Collins, CO

Limber pine (Pinus flexilis James) and Rocky Mountain bristlecone pine

(Pinus aristata Engelm.) are two white pine species that grow in Colorado.

Limber pine is distributed throughout western North America while bristlecone pine’s distribution is almost entirely within Colorado. Limber pine grows

from lower treeline to upper treeline (1600 m to 3400 m) in Colorado while

bristlecone pine is restricted to the subalpine and upper treeline areas (2750 m

to 3670 m). Both species are seral on mesic sites and form persistent stands on

xeric sites. White pine blister rust (Cronartium ribicola) was introduced to

the Vancouver area of North America in the early 1900s and is spreading

southward through the white pine ecosystems. White pine blister rust was

identified in Colorado in 1998 on limber pine and has not yet been reported

on bristlecone pine.

Fire may provide a management tool to aid in the coexistence of white pine

populations with the rust. Both pines are pioneer species that depend on fire

to open sites for regeneration. In the presence of blister rust, these colonization sites offer opportunity for selection of rust-resistant genotypes. The

balanced application of fire to increase colonization opportunities while preserving seed sources may accelerate the development of rust-resistant white

pine populations. The incomplete understanding of the ecology of limber and

bristlecone pine constrain our ability to rapidly develop and implement management and conservation programs. An interdisciplinary research program

applying ecological, physiological, and meta-population approaches has been initiated to improve our understanding of the regeneration dynamics, population

genetics, rust resistance, and adaptive variation of high elevation white pines to

facilitate our ability to develop potential management and conservation options.

Effects of Fall Vs. Summer Prescribed Burns in

SW Ponderosa Pine Forests: A Case Study

from Saguaro National Park, Arizona

Kathy Schon, National Park Service, Saguaro National Park, Tucson, AZ

Two years of fire effects monitoring data are analyzed to evaluate changes

in ponderosa pine forest structure, composition, fuel loading, and vegetation

Poster Abstracts
462 USDA Forest Service Proceedings RMRS-P-29. 2003.

characteristics of Saguaro National Park in southeast Arizona. In June of 1993

and 1996 two prescribed burns were conducted in the Rincon Mountains of

Saguaro National Park. In October 1996, 1997, and 1998, three more prescribed burns were implemented in the same mountain range. All five burns

were in ponderosa pine forests between 7000 and 8600 ft elevation. The burn

units were ignited using strip-firing techniques. Minimal mechanical treatment was applied around the burn perimeters to secure the fire line. Twenty-five

50x20m fire effects monitoring plots were installed throughout the burn units

to monitor trends and evaluate objectives. These plots were burned in the

same manner as the rest of the burn unit and no mechanical treatment was

applied in or within 50 m of each plot. Preliminary data have shown similar

results for both summer and fall burns in reducing fuels and measurable differences in reducing pole sized trees and overstory tree mortality. Preliminary

herbaceous data have shown an increase in herbaceous cover with fall burning

and a reduction with summer burning.

Building the Scientific Basis for Fuel

Treatments and Restoration in the Southwest

Martha Schumann and Tori Derr, Southwest Community Forestry

Research Center, Forest Trust, Santa Fe, NM

We are undertaking a project to describe and compare fuel reduction and

restoration treatments implemented in ponderosa pine forests in New Mexico,

Arizona, and Colorado. The analysis will evaluate whether the prescription

and outcome meet the treatment objectives. Preliminary data from sites show

a wide range of site conditions, prescriptions, and treatments. This project will

contribute to the development of appropriate prescription guidelines for the

Southwest. We have also developed a protocol for community-based assessments of fuel reduction projects, composed of a framework of monitoring

questions, and methods for data collection and analysis. Protocols will be

piloted in two areas on the Cibola National Forest. The goal of assessing and

monitoring treatments is to build a regional knowledge base shared between

communities, scientists, and managers to inform forest management.

First Year Postfire Erosion Rates on the

Bitterroot National Forest

Kevin M. Spigel, Department of Geography, University of WisconsinMadison, Madison, WI; and Peter R. Robichaud, USDA Forest

Service, Rocky Mountain Research Station, Moscow, ID

Accelerated runoff and erosion are common occurrences following forest

fires due to loss of protective forest floor material. The combustion of organic

forest floor material may cause high percentages of bare soil exposed to overland flow, raindrop impact, and water repellent soil conditions. Twenty-four

silt fences were installed to study postfire erosion on a storm-by-storm basis

on the Bitterroot National Forest in Western Montana after the 2000 fire

season. This study focused on erosion rates on steep slopes between two burn

severities and two stand densities. Erosion rates were determined from sediment trapped behind the silt fences from a contributing area of 100 m2

.

Sediment collected was weighed onsite and a sub-sample taken for further

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 463

analysis. Short duration high intensity thunderstorms (10-min max. intensity

79 mm hr-1) caused the highest erosion rates (80 t ha-1). Whereas the long

duration low intensity rain events produced little erosion (0.01 t ha-1).

Fire and Fire Surrogate Treatments for

Ecosystem Restoration

Scott Stephens, Division of Forest Science, Department of

Environmental Science, Policy, and Management, University of

California, Berkeley, CA; and James McIver, USDA Forest Service,

Pacific Northwest Research Station, La Grande, OR

Compared to presettlement times, many dry forests of the United States

are now more dense and have greater quantities of fuels. Widespread treatments are needed in these forests to restore ecological integrity and to reduce

the risk of uncharacteristically severe fires. Among possible treatments, however, the appropriate balance among cuttings, mechanical fuel treatments, and

prescribed fire is often unclear. Resource managers need better information

on the comparative effects of alternative practices such as prescribed fire and

mechanical “fire surrogates.” An integrated national network of 13 long-term

research sites has been established to address this need, with support from the

U.S. Joint Fire Science Program and the National Fire Plan. Four alternative

treatments will be applied in replication at each site: (1) cuttings and mechanical fuel treatments alone; (2) prescribed fire alone; (3) a combination of cuttings,

mechanical fuel treatments, and prescribed fire; and (4) untreated controls.

Response to treatment will be determined through the repeated measurement

of a comprehensive set of core variables at each site, including aspects of fire

behavior and fuels, vegetation, wildlife, entomology, pathology, soils, and economics. The experiment is designed to facilitate inter-disciplinary analysis at

the site level and meta-analysis for each discipline at the national level. The

inter-disciplinary nature of the study will provide managers with information

on how their practices affect whole ecosystems, while meta-analysis will provide insight on which responses are general, and which are dependent on

specific environmental conditions. At present, two sites have collected posttreatment data, five are in the midst of treatment application, and the remaining

six have collected all pre-treatment data and are poised to apply treatments.

Identifying the Most Appropriate Decision

Framework for Assessing Fire and Fuel

Management Options

David A. Tallmon, Danny C. Lee, Steve Norman, and Christine May,

USDA Forest Service, Redwood Sciences Lab, Arcata, CA

Fire and fuels management decisions can be very complex. The complexity

inherent in these decisions often comes from the ecological, social, and economic issues that surround them. We identify and describe a number of different

decision processes or frameworks that are available to decision-makers to help

them articulate and explore the possible effects and risks of management

actions for competing issues. The decision processes vary in terms of their

complexity, sophistication, and underlying philosophies. We evaluate several

Poster Abstracts
464 USDA Forest Service Proceedings RMRS-P-29. 2003.

of these different decision processes using simple criteria and suggest which

ones are most appropriate for each of the institutional levels at which fire and

fuels management decisions must be made.

Fuel Load Changes Associated With PinyonJuniper Woodland Expansion in Central

Nevada

Robin J. Tausch, USDA Forest Service, Rocky Mountain Research

Station, Reno, NV; and Alicia Reiner, Department of Environmental

and Resource Sciences, University of Nevada-Reno, Reno, NV

Since European settlement there has been a pronounced increase in distribution and dominance of Great Basin pinyon-juniper woodlands. Two-thirds

to three-quarters are post-settlement in age. Largely attributed to the reduced

occurrence of fire resulting from the heavy livestock grazing that followed

settlement, this expansion is impacting a wide range of sagebrush ecosystems.

Dramatic changes in the levels and types of fuels present, and increases in fire

size and severity, are resulting. Fuel load changes responsible for these changes

have not been quantified. Studies of the expansion and growth of woodlands

at a central Nevada location provide a first look at the patterns of increasing

fuel loads over time. At mid successional stages, fuel loads can be increased by

500 lbs. per acre per year. At full tree dominance, total fuel loads can be 10 or

more times those of the former sagebrush ecosystem.

Using Prescribed Fire for Fuel Reduction and

Ecological Restoration

Laura Trader and Sarah DeMay, Bandelier National Monument,

Los Alamos, NM

Research shows that fire suppression has resulted in dramatic changes in

the fire regimes at Bandelier National Monument, NM, and has produced

significant ecological effects on the fire-prone landscapes. High accumulations of woody fuels and increased tree densities are a few of these effects.

These unnatural conditions have resulted in catastrophic fire events in areas,

such as Bandelier’s ponderosa pine forests, where they were once anomalous.

In 1977, the stand-destroying La Mesa Fire consumed 16,000 acres in

Bandelier’s ponderosa pine forest. This fire event has changed the landscape

from a pine forest to a grass/shrubland. In 1998, 21 years post fire, Bandelier

fire and resource managers decided to use fire in this area to reduce the accumulations of woody fuels, reduce seedling tree densities, and increase native

plant cover. Our poster presents the 2-year post burn results from data collected on permanent vegetation plots in this area. It appears that prescribed

fire has been effective in reducing woody fuels and promoting forest health.

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 465

Importance of Fire Occurrence in Simulation

of Fuel Landscapes on the Angeles National

Forest, Southern California

David R. Weise, M. Arbaugh, USDA Forest Service, Pacific

Southwest Research Station, Forest Fire Laboratory, Riverside, CA;

J. Chew, G. J. Jones, USDA Forest Service, Rocky Mountain Research

Station, Fire Sciences Laboratory, Missoula, MT; M. Wiitala, USDA

Forest Service, Pacific Southwest Research Station, Forest Fire

Laboratory, Riverside, CA; J. Merzenich, USDA Forest Service,

Portland, OR; J. van Wagtendonk, USGS, Western Ecological

Research Center, Yosemite Field Station, El Portal, CA; M. Schaaf,

Air Sciences, Inc, Portland, OR; S. Schilling, USDA Forest Service,

Pacific Southwest Research Station, Forest Fire Laboratory,

Riverside, CA; J. Sullivan, USDA Forest Service, Rocky Mountain

Research Station, Fire Sciences Laboratory, Missoula, MT; and

R. Kimberlin, USDA Forest Service, Pacific Southwest Research

Station, Forest Fire Laboratory, Riverside, CA

Understanding the trade-offs between short-term and long-term consequences of fuel treatments on ecosystems is needed before a comprehensive

fuels management program can be implemented nationally. We are evaluating

three potential trade-off models at eight locations in major U.S. fuel types.

The selected models/modeling approaches are (1) the Fire Effects Trade-off

Model (FETM), (2) sequential use of the SIMPPLLE and MAGIS models,

and (3) the Vegetation Dynamics Development Tool (VDDT). Each model

simulates natural succession and vegetation changes following various disturbances including wildfire. We are evaluating the sensitivity of these models to

various inputs including description of fire occurrence. Fire occurrence rates

estimated from data for the Angeles National Forest were multiplied by 0.25,

0.5, 2, and 4. One hundred years of vegetation change were simulated by each

model using the modified fire occurrence rates. The effects of change in fire

occurrence rate on simulated wildfire acreage, vegetation composition, and

smoke emissions will be presented.

Poster Abstracts
466 USDA Forest Service Proceedings RMRS-P-29. 2003.

Simulation of Historical Fires and Their Impact

on Fuels in Yosemite National Park

David R. Weise, USDA Forest Service, Pacific Southwest Research

Station, Forest Fire Laboratory, Riverside, CA; J. van Wagtendonk,

USGS Western Ecological Research Center, Yosemite Field Station,

El Portal, CA; M. Arbaugh, USDA Forest Service, Pacific Southwest

Research Station, Forest Fire Laboratory, Riverside, CA; J. Chew,

G. J. Jones, USDA Forest Service, Rocky Mountain Research Station,

Fire Sciences Laboratory, Missoula, MT; M. Wiitala, USDA Forest

Service, Pacific Southwest Research Station, Forest Fire Laboratory,

Riverside, CA; J. Merzenich, USDA Forest Service, Portland, OR;

M. Schaaf, Air Sciences, Inc, Portland, OR; S. Schilling, USDA Forest

Service, Pacific Southwest Research Station, Forest Fire Laboratory,

Riverside, CA; J. Sullivan, USGS Western Ecological Research Center,

Yosemite Field Station, El Portal, CA; and R. Kimberlin, USDA Forest

Service Pacific Southwest Research Station, Forest Fire Laboratory,

Riverside, CA

Understanding the trade-offs between short-term and long-term consequences of fuel treatments on ecosystems is needed before a comprehensive

fuels management program can be implemented nationally. We are evaluating

3 potential trade-off models at 8 locations in major U.S. fuel types. The selected models/modeling approaches are 1) the Fire Effects Trade-off Model

(FETM), 2) sequential use of the SIMPPLLE and MAGIS models, and 3) the

Vegetation Dynamics Development Tool (VDDT). Each model simulates natural succession and vegetation changes following various disturbances including

wildfire. Since it is not currently possible to check the reliability of predicted

fuel conditions in 50 years, we are conducting a historical retrospective study

at Yosemite National Park. We have simulated fire occurrence using historical

data from Yosemite NP and simulated the changes in landscape composition,

wildfire acreage, and smoke emissions using each of the three models. Preliminary results of the retrospective study will be displayed.

Understory Vegetation Response to Fire and

Overstory Reduction in Black Hills Ponderosa

Pine Stands

Cody L. Wienk, USDI National Park Service, Wind Cave National

Park, Hot Springs, SD and School of Renewable Natural Resources,

University of Arizona, Tucson, AZ; Guy R. McPherson, School of

Renewable Natural Resources, University of Arizona, Tucson, AZ;

and Carolyn Hull Sieg, USDA Forest Service, Rocky Mountain

Research Station, Flagstaff, AZ

The role and extent of fire in ponderosa pine forests in the Black Hills,

South Dakota, has been reduced in the last century. In some dense stands, a

thick layer of pine needles has replaced understory vegetation. We experimentally addressed the effects of prescribed burning and overstory reduction on

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 467

understory vegetation, and examined the extent to which lack of a soil seed

bank constrained understory recruitment. Response of understory vegetation

during the first growing season post-treatments was sparse. There were, however, significant treatment effects during the second growing season. Total

understory biomass ranged from 5.8 kg/ha on untreated plots to 1724 kg/ha

on clearcut, unburned plots. Herbaceous dicots comprised over 90 percent

of the total understory biomass. Only 57 individual plants, or 186 seeds/m2

,

emerged from 1080 soil samples. Nonetheless, paucity of viable seeds in the

soil seed bank does not appear to constrain recruitment of understory vegetation in dense ponderosa pine forests of South Dakota.

Equipment to Reduce Submerchantable

Biomass in Anticipation of Future Prescribed

Fire Treatments

Keith Windell, Missoula Technology & Development Center,

Missoula, MT; and Sunni Bradshaw, USDA Forest Service, Rocky

Mountain Research Station, Fire Sciences Laboratory, Missoula, MT

The USDA Forest Service’s Technology and Development Center in

Missoula, Montana, was asked to identify or develop equipment and techniques that would help managers prepare difficult sites for future prescribed

fire treatments. These difficult sites have fuel loadings and distributions that

increase the chance of fire escape and unacceptable resource damage. Much of

the equipment identified during this project has direct application for fuels

reduction in the wildland urban interface. The results of this project have

been documented in two publications: Understory Biomass Reduction Methods and Equipment (0051-2828-MTDC), and Understory Biomass Reduction

Methods and Equipment Catalog (0051-2826-MTDC). The reports document operational characteristics, capabilities, and limitations of equipment with

potential to reduce biomass.

Restoring the Mixed-Oak Forest With

Prescribed Fire and Thinning

Daniel Yaussy and Mary Boda, USDA Forest Service, Delaware, OH

The oak forest type is one of the largest endangered ecosystems. Central

hardwoods ecosystems dominated by an oak overstory are seldom replaced

with oak regeneration, regardless of harvesting method. This failure in oak

development is the result of changed disturbance regimes, in particular, the

suppression of human-caused fire since the 1930s. Studies of prescribed fires

have established that fire alone will not remove the larger midstory trees that

inhibit oak regeneration. As part of the national Fire and Fire Surrogate (FFS)

study, four treatments have been implemented: control, fire only, thinning

only, and fire plus thinning. In addition to the integrated ecosystem studies

required by the FFS study protocols, collateral investigations include the effects of the treatments on: acorn production and predation by weevils and

deer, bats, and flying squirrels; mycorrhizae; fire intensity; and soil moisture.

Poster Abstracts
468 USDA Forest Service Proceedings RMRS-P-29. 2003.

Modernizing Fire Weather Data Collection:

The RAWS Review

John Zachariassen and Karl F. Zeller, USDA Forest Service Rocky

Mountain Research Station, Fort Collins, CO; Thomas McClelland,

USDA Forest Service, National Weather Program, Washington, DC;

and Richard W. Fisher, USDA Forest Service Air Program, Washington,

DC

The Remote Automated Weather Station (RAWS) network has been providing weather data to the United States Forest Service (USFS) and other

land management agencies for the past 23 years. Prior to that, most fire weather

observations were taken manually and a few made automatically with a RAWS

prototype. Current USFS requirements for weather information have grown

from strictly fire support – for which RAWS was originally designed and deployed – to significantly broader applications and uses. For example: physical

and regional climatology, weather forecasting, support of air quality and pollutant monitoring in the atmospheric boundary layer, aerosol and trace gas

flux measurements, environmental aerodynamics, and ecosystem (process)

modeling. Even though current and future fire applications will continue to

be the primary use for RAWS data, the growing list of uses for weather data

presents an urgent need to assess the RAWS network. Such an assessment will

include reviewing the individual stations themselves for density, distribution,

redundancy, and overall function in addition to the RAWS system’s ability to

provide the Forest Service with the meteorological data and weather intelligence products it needs now and into the future. Preliminary results indicate

that the RAWS network is a national asset and functioning fairly well given

that it is a multi-agency network and with many user/owner choices for funding and operating individual stations. Individual stations meeting National

Fire Danger Rating System standards are providing data in support of fire

weather forecasting and for calculating fire danger rating indices and components. The entire network is in perpetual transition, undergoing hardware and

software upgrades as well as streamlining data transmission.

Measuring, Modeling, and Tracking Western

Spruce-Fir Forest Water Vapor Fluxes as a Fire

Danger Indicator

Karl Zeller, USDA Forest Service, Rocky Mountain Research Station,

Fort Collins, CO; Ned Nikolov, USGS, Fort Collins, CO; and John

Zachariassen, USDA Forest Service, Rocky Mountain Research

Station, Fort Collins, CO

Measurements of vertical water vapor flux made by eddy covariance were

taken at 23-meter height above a subalpine spruce-fir forest in the USDA

Forest Service’s Glacier Lakes Ecosystem Experiment Site, Snowy Range,

Wyoming.

Seasonal and diurnal water-vapor fluxes were studied. Seasonally during the

growing season, water fluxes had much larger amplitudes. The FORFLUX

Poster Abstracts
USDA Forest Service Proceedings RMRS-P-29. 2003. 469

biophysical model was used to analyze eddy covariance flux measurements

and track water availability in different components of the ecosystem (e.g.

soil, ground litter, and vegetation) on an hourly basis. Seasonal and interannual variations of ecosystem water fluxes and water content in vegetation

and litter can provide indications for critical ecosystem drying and help improve assessments of potentially fire danger.

The FORFLUX model mechanistically couples all major ecosystem processes and predicts seasonal dynamics of water vapor, carbon dioxide (CO2),

and ozone (O3) exchange between a terrestrial ecosystem and the atmosphere.

FORFLUX consists of four interconnected modules: leaf photosynthesis;

canopy flux; soil heat-, water- and CO2- transport; and snowpack. All biophysical interactions are computed hourly while model projections can be

output for either hourly or daily time steps.

FORFLUX requires input for: climate, soils, vegetation structure, species

physiology (20 parameters); hourly ambient temperature, relative humidity,

incident short-wave radiation, precipitation, above-canopy wind speed; soil

texture, depth, and volumetric rock content; and canopy leaf area index and

foliage clumping factor. Output: net ecosystem water, carbon, and ozone fluxes

and their components, such as vegetation net primary production (NPP), plant

ozone uptake, canopy photosynthesis and stomatal conductance, woody respiration, soil CO2 efflux, canopy transpiration and rainfall interception, soil

evaporation, snow melt and sublimation, surface runoff, and subsoil drainage.

Poster Abstracts
470 USDA Forest Service Proceedings RMRS-P-29. 2003.
USDA Forest Service Proceedings RMRS-P-29. 2003. 471

Suzanne Acton

USFS Payette National Forest

B.O. Box J

New Meadows, ID 83638

208-347-0336

sacton@fs.fed.us

Mike Alexander

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-472-0479

malexand@cnr.colostate.edu

Greg Aplet

Wilderness Society

7475å Dakin Street, Suite 410

Denver, CO 80221

303-650-5818

greg_aplet@tws.org

Rob Avera

Colorado State University

113 Forestry BLDG

Fort Collins, CO 80523

970-491-3194

rob@cnr.colostate.edu

Craig C. Axtell

National Park Service

1201 Oakridge Drive, Suite 200

Fort Collins, CO 80525

970-225-3591

craig_axtell@nps.gov

Bill Baker

University of Wyoming

Department of Geography

Laramie, WY 82071

307-766-2925

bakerwl@uwyo.edu

Noah Barstatis

USFS Rocky Mountain Research

Station

2500 S. Pine Knoll Drive

Flagstaff, AZ 86001-6381

928-556-2172

nbarstatis@fs.fed.us

Mike Battaglia

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1315

mbattaglia@fs.fed.us

Melissa Borsting

University of Washington

Box 352100

Seattle, WA 98195-2100

206-543-5769

borsting@u.washington.edu

Peter Brown

Rocky Mountain Tree-Ring Research

2901 Moore Lane

Fort Collins, CO 80526

970-229-9557

pmb@rmtrr.org

Kevin Buckley

Los Alamos National Laboratory

P.O. Box 1336, MS 87544

Los Alamos, NM 87544

505-667-1454

k.buckley@lanl.gov

Sam Burns

Fort Lewis College, Office of

Community Services

1000 Rim Drive

Durango, CO 81301

970-247-7193

burns_s@fortlewis.edu

Gabriel Chapin

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-494-0378

gchapin@holly.colostate.edu

Linda Chappell

Central Utah Interagency Fire

150 E 900 N

Richfield, UT 84701

435-896-1059

lchappell@fs.fed.us

Tony Cheng

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-1900

chengt@cnr.colostate.edu

Jim Chew

Rocky Mountain Research Station

P.O. Box 8089

Missoula, MT 59807

406-542-4171

jchew@fs.fed.us

Wallace Covington

Northern Arizona University

Ecological Restoration Institute

Flagstaff, AZ 86011-5017

928-523-6635

wallace.covington@nau.edu

Miguel Cruz

University of Montana

460 Science Complex

Missoula, MT 59812

406-243-6422

mgcruz@selway.unt.edu

Mike daLuz

USFS, Rocky Mountain Region

740 Simms St., P.O. Box 25127

Lakewood, CO 80225

303-275-5749

mdaluz@fs.fed.us

Bob Davis

Bureau of Land Management

345 East Riverside Drive

St. George, UT 84790

435-688-3285

robert_davis@blm.gov

Sarah DeMay

Bandelier National Monument, NPS

HCR1, Box1, Suite 15

Los Alamos, NM 87544

505-672-3648 X28

smargotd@hotmail.com

Author Contact List
472 USDA Forest Service Proceedings RMRS-P-29. 2003.

Tim Duck

Parashant National Monument, NPS

345 E. Riverside Drive

St. George, UT 84790

435-688-3238

tim_duck@blm.gov

Don Falk

Laboratory of Tree Ring Research,

University of Arizona

105 West Stadium, Bldg 58T

Tucson, AZ 85721

520-626-7201

dafalk@u.arizona.edu

Carl Fiedler

University of Montana

School of Forestry

Missoula, MT 59812

406-243-5602

fiedler@forestry.umt.edu

Mark A. Finney

USFS Forest Sciences Lab

800 East Beckwith, P.O. Box 8089

Missoula, MT 59807

406-329-4832

mfinney@fs.fed.us

Jo Ann Fites

U.S. Forest Service

631 Coyote Street

Nevada City, CA 95959

530-478-6151

jfites@fs.fed.us

Paula Fornwalt

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-2581

pfornwalt@fs.fed.us

John Frank

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1319

jfrank@fs.fed.us

David Ganz

University of California Berkeley

201 Wellman Hall, #3112

Berkeley, CA 94720-3112

415-602-1395

daveganz@nature.berkeley.edu

Paul Gleason (deceased)

Colorado State University

Forest Sciences Department

Fort Collins, CO 80523

Armando Gonzalez-Caban

USFS Forest Fire Lab

511 Athens Street

Riverside, CA 92507

909-680-1525

agonzalescaban@fs.fed.us

Stephanie Gripne

University of Montana

School of Forestry

Missoula, MT 59812

Wendell Hann

USFS Gila National Forest

3005 E. Camino Del Bosque

Silver City, NM 88061

505-388-8243

whann@fs.fed.us

Jennifer J. Henry

University of Arkansas-Monticello

P.O. Box 3468

Monticello, AR 71655

870-367-9254

jenhen3@hotmail.com

Hayley Hesseln

University of Montana

School of Forestry

Missoula, MT 59812-0576

406-243-4285

hayley@forestry.umt.edu

Christy Higgason

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-407-1289

christy@cnr.colostate.edu

Mike Hilbruner

U.S. Forest Service

381 Magothy Road

Severna Park, MD 21146

202-205-1487

mhilbruner@fs.fed.us

John Hof

Rocky Mountain Research Station

2150A Centre Ave, Suite 361

Fort Collins, CO 80526

970-295-5977

jhof@fs.fed.us

Laurie Stroh Huckaby

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1298

lhuckaby@fs.fed.us

Molly Hunter

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-0614

mhunter@cnr.colostate.edu

Timothy Ingalsbee

Western Fire Ecology Center

P.O. Box 51026

Eugene, OR 97405

541-302-6218

fire@efn.org

Greg Jones

Rocky Mountain Research Station

P.O. Box 8089

Missoula, MT 59807

406-542-4167

jgjones@fs.fed.us

Linda Joyce

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-2560

ljoyce@fs.fed.us

Mohammed Kalkhan

Colorado State University

Natural Resource Ecology Lab

Fort Collins, CO 80523

970-491-5262

mohammed@nrel.colostate.edu

Merrill Kaufmann

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1256

mkaufmann@fs.fed.us

Steve Kilpatrick

Wyoming Game and Fish Dept.

420 North Cash

Jackson, WY 83001

307-733-2383

steve.kilpatrick@wgf.wy.us

Author Contact List
USDA Forest Service Proceedings RMRS-P-29. 2003. 473

John Korfmacher

USFS Rocky Mountain Research

Station

920 Valley Road

Reno, NV 89512

775-784-5329

jkorfmacher@fs.fed.us

Tasha Kotliar

U.S. Geological Survey

2150 Centre Avenue, Bldg C

Fort Collins, CO 80526

970-226-9446

tasha_kotliar@usgs.gov

Leigh Lentile

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-2796

lentile@cnr.colostate.edu

Reese Lolley

University of Washington

902 N 81st

Seattle, WA 98103

206-543-5769

rlolley@u.washington.edu

Don Long

USFS Rocky Mountain Research

Station

P.O. Box 8089

Missoula, MT 59807

406-329-4706

dlong01@fs.fed.us

John Loomis

Colorado State University

Dept of Agricultural and Resource

Economics

Fort Collins, CO 80523

970-491-2485

jloomis@agsci.colostate.edu

Kurt Mackes

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-4066

kmackes@cnr.colostate.edu

Ann Maclean

School of Forestry & Wood Products

Michigan Tech University

1400 Townsend Drive

Houghton, MI 49931

906-487-2030

amaclean@mtu.edu

Erik Martinson

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-1779

erikm@cnr.colostate.edu

William Massman

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1296

wmassman@fs.fed.us

Ward McCaughey

Rocky Mountain Research Station

800 E. Beckwith, P.O. Box 8089

Missoula, MT 59807

406-329-2125

wmccaughey@fs.fed.us

Jim Menakis

USFS Rocky Mountain Research

Station

P.O. Box 8089

Missoula, MT 59807

406-329-4958

jmenakis@fs.fed.us

Carol Miller

Aldo Leopold Wilderness Institute

P.O. Box 8089

Missoula, MT 59807

406-542-4198

cmiller04@fs.fed.us

Ken Moore

Bureau of Land Management

345 E. Riverside Drive

St. George, UT 84790

435-688-3267

ken_moore@blm.gov

Dan Neary

Rocky Mountain Research Station

21500 South Pine Knoll Drive

Flagstaff, AZ 86001

928-556-2176

dneary@fs.fed.us

Jose Negron

Rocky Mountain Research Station

240 West Prospect

Fort Collins, CO 80526

970-498-1252

jnegron@fs.fed.us

Steve Norman

USFS Redwood Sciences Lab

1700 Bayview Dr.

Arcata, CA 95521

707-826-1276

stevenorman@fs.fed.us

Phil Omi

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-5819

phil@cnr.colostate.edu

William Otrosina

U.S. Forest Service

320 Green Street

Athens, GA 30602

706-559-4290

wotrosina@fs.fed.us

R. Brian Paul

Los Alamos National Laboratory

P.O. Box 1663, MS M769

Los Alamos, NM 87544

505-665-8041

rpaul@lanl.gov

Dan Perrakis

University of Washington

Box 352100

Seattle, WA 98115

206-543-5769

perrakis@u.washington.edu

Jeff Prestemon

Southern Research Station

P.O. Box 12254, 3041 E.

Cornwallis Road

Research Triangle Park, NC 27709

919-549-4093

jprestemon@fs.fed.us

John Pye

Southern Research Station

P.O. Box 12254, 3041 E.

Cornwallis Road

Research Triangle Park, NC 27709

919-549-4013

jpye@fs.fed.us

Author Contact List
474 USDA Forest Service Proceedings RMRS-P-29. 2003.

Doug Rideout

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-7234

doug@cnr.colostate.edu

Matt Rollins

USFS Rocky Mountain Research

Station

P.O. Box 8089

Missoula, MT 59807

406-329-4960

mrollins@fs.fed.us

Bill Romme

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523

970-491-2870

romme@cnr.colostate.edu

Anna W. Schoettle

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1333

aschoettle@fs.fed.us

Kathy Schon

Saguaro National Park, NPS

3693 South Old Spanish Trail

Tucson, AZ 85716

520-622-2602

kathy_schon@nps.gov

Martha Schumann

Forest Trust

P.O. Box 519

Santa Fe, NM 87504

505-983-8992 x23

martha@theforesttrust.org

Joe Scott

Systems for Environmental

Management

520 Ford Street

Missoula, MT 59801

406-329-4837

Joe.Scott@montana.com

Dave Scott

U.S. Forest Service

340 N. Cache St, P.O. Box 1888

Jackson, WY 83001

307-739-5562

dtscott@fs.fed.us

Francisco Senra Rivero

Colorado State University

Department of Forest, Range and

Watershed Stewardship

Fort Collins, CO 80523-1470

970-491-3475

fsenra@cnr.colostate.edu

Wayne D. Shepperd

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1259

wshepperd@fs.fed.us

Carolyn Hull Sieg

USFS Rocky Mountain Research

Station

2500 S. Pine Knoll Drive

Flagstaff, AZ 86001

928-556-2151

csieg@fs.fed.us

Kevin Spigel

University of Wisconsin, Department

of Geography

Room 384, Science Hall

Madison, WI 53706

608-262-8920

kmspigel@students.wisc.edu

Susan Stafford

Colorado State University

Forest Sciences Department

Fort Collins, CO 80523

970-491-6911

stafford@cnr.colostate.edu

Scott Stephens

University of California Berkeley

ESPM Forest Science, 145 Mulford

Hall, MC 3114

Berkeley, CA 94720-3114

510-642-7304

stephens@nature.berkeley.edu

Jason Stoker

USGS EROS Data Center

47914 252nd Street

Sioux Falls, SD 57198

605-594-2579

jstoker@usgs.gov

Diane J. Strohm

Pike-San Isabel National Forest

1920 Valley Drive

Pueblo, CO 81008

719-585-3710

dstohm@fs.fed.us

David Tallmon

USFS Redwood Sciences Lab

1700 Bayview Dr.

Arcata, CA 95521

707-826-1276

dtallmon@fs.fed.us

Robin Tausch

Rocky Mountain Research Station

920 Valley Road

Reno, NV 89512

775-784-5329

rtausch@fs.fed.us

Eric Toman

Oregon State University

Forest Sciences Department

Corvallis, OR 97331

541-737-2215

eric.toman@orst.edu

Thomas T. Veblen

University of Colorado

Department of Geography

Boulder, CO 80309-0260

303-492-8528

veblen@spot.colorado.edu

Linda Wadleigh

U.S. Forest Service

800 S. 6th Street

Williams, AZ 86004

928-635-8351

lwadleigh@fs.fed.us

Jeff Walterscheid

Los Alamos National Laboratory

300 Donna

Los Alamos, NM 87544

505-699-0936

jeffw@lanl.gov

Author Contact List
USDA Forest Service Proceedings RMRS-P-29. 2003. 475

Keith Windell

USFS Missoula Technology &

Development Center

Bldg 1, Fort Missoula

Missoula, MT 59804

406-329-3956

kwindell@fs.fed.us

Dan Yaussy

U.S. Forest Service

359 Main Road

Delaware, OH 43015-8640

740-368-0101

dyaussy@fs.fed.us

John Zachariassen

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1050

jzachariassen@fs.fed.us

Steve Zack

Wildlife Conservation Society

1746 NW Hartwell Place

Portland, OR 97229

503-644-8727

SteveZack@msn.com

Karl Zeller

USFS Rocky Mountain Research

Station

240 West Prospect

Fort Collins, CO 80526

970-498-1238

kzeller@fs.fed.us

Tom Zimmerman

National Park Service

3833 S. Development Ave

Boise, ID 83705

208-387-5215

Tom_Zimmerman@nps.gov

Author Contact List
The Rocky Mountain Research Station develops scientific information and technology to improve management, protection,

and use of the forests and rangelands. Research is designed to

meet the needs of National Forest managers, Federal and State

agencies, public and private organizations, academic institutions,

industry, and individuals.

Studies accelerate solutions to problems involving ecosystems,

range, forests, water, recreation, fire, resource inventory, land reclamation, community sustainability, forest engineering technology, multiple use economics, wildlife and fish habitat, and forest

insects and diseases. Studies are conducted cooperatively, and

applications may be found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

Lincoln, Nebraska Laramie, Wyoming

*Station Headquarters, Natural Resources Research Center,

2150 Centre Avenue, Building A, Fort Collins, CO 80526

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color,

national origin, sex, religion, age, disability, political beliefs, sexual

orientation, or marital or family status. (Not all prohibited bases

apply to all programs.) Persons with disabilities who require alternative means for communication of program information

(Braille, large print, audiotape, etc.) should contact USDA's TARGET Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director,

Office of Civil Rights, Room 326-W, Whitten Building, 1400 Independence Avenue, SW, Washington, DC 20250-9410 or call

(202) 720-5964 (voice or TDD). USDA is an equal opportunity

provider and employer.

ROCKY MOUNTAIN RESEARCH STATION

RMRS

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