USDA United States

-

=T Department of

 Agriculture Assessing the Effects of Fire

Forest Service

Pacific Northwest Disturbance on Ecosystems: A

Research Station

General Technical Scientific Agenda for Research

Report

PNW-GTR-455

May 1999 and Management

Daniel L. Schmoldt, David L. Peterson, Robert E. Keane,

James M. Lenihan, Donald McKenzie, David R. Weise,

and David V. Sandberg
Authors DANIEL L. SCHMOLDT is a research forest products technologist with the U.S. Department of

Agriculture, Forest Service, Southern Research Station, Biological Systems Engineering Department, 460 Henry Hall, University of Wisconsin, Madison, WI 53706-1561; DAVID L. PETERSON

is a research biologist and professor, U.S. Geological Survey, Forest and Rangeland Ecosystem

Science Center, and DONALD MCKENZIE is a research scientist, University of Washington Field

Station, Box 352100, Seattle, WA 96195; ROBERT E. KEANE is a research forester, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory,

PO Box 8089, Missoula, MT 59807; JAMES M. LENIHAN is a postdoctoral research associate,

Oregon State University, VO of Pacific Northwest Research Station, Forestry Sciences Laboratory,

3200 SW Jefferson Way, Corvallis, OR 97331; DAVID R. WEISE is a research forester and

project leader, U.S. Department of Agriculture, Forest Service, Pacific Southwest Research

Station, Riverside Fire Laboratory, 4955 Canyon Crest Dr., Riverside, CA 92507; and DAVID V.

SANDBERG is a research forester and team leader, U.S. Department of Agriculture, Forest

Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331.
Abstract Schmoldt, Daniel L.; Peterson, David L.; Keane, Robert E.; Lenihan, James M.;

McKenzie, Donald; Weise, David I?.; Sandberg, David V. 1999. Assessing the

effects of fire disturbance on ecosystems: a scientific agenda for research and

management. Gen. Tech. Rep. PNW-GTR-455. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 104 p.

A team of fire scientists and resource managers convened 17-l 9 April 1996 in Seattle,

Washington, to assess the effects of fire disturbance on ecosystems. Objectives of

this workshop were to develop scientific recommendations for future fire research and

management activities. These recommendations included a series of numerically

ranked scientific and managerial questions and responses focusing on (1) links among

fire effects, fuels, and climate; (2) fire as a large-scale disturbance; (3) fire-effects

modeling structures; and (4) managerial concerns, applications, and decision support.

At the present time, understanding of fire effects and the ability to extrapolate fireeffects knowledge to large spatial scales are limited, because most data have been

collected at small spatial scales for specific applications. Although we clearly need

more large-scale fire-effects data, it will be more expedient to concentrate efforts on

improving and linking existing models that simulate fire effects in a georeferenced

format while integrating empirical data as they become available. A significant component of this effort should be improved communication between modelers and

managers to develop modeling tools to use in a planning context. Another component

of this modeling effort should improve our ability to predict the interactions of fire and

potential climatic change at very large spatial scales. The priority issues and approaches described here provide a template for fire science and fire management

programs in the next decade and beyond.

Keywords: Analytic hierarchy process, ecological disturbance, fire effects, large-scale

fire, modeling.
Summary Fire and other large-scale disturbances have become an increasingly important issue

as scientists, resource managers, and society begin to embrace ecosystem-based

management of natural resources. Although fire is recognized as an important component of ecosystem dynamics, the effects of infrequent, large-scale fire events have

been difficult to quantify and model. Most fire-effects data have been collected at

small spatial scales, but demands are increasing for large-scale applications in fire

science and resource management. This leads to the potential for propagating

substantial errors when extrapolating limited data to large spatial scales.

Future scientific efforts relevant to large-scale fire disturbance must encompass the

concerns of both scientists and resource managers and should be prioritized and

sequenced logically. This document describes the output of a workshop in which a

team of fire scientists and public land managers developed an agenda for high-priority

issues and activities relevant to fire disturbance. Individual working groups focused

on (1) links among fire effects, fuels, and climate; (2) fire as a large-scale disturbance;

(3) fire-effects modeling structures; and (4) managerial concerns, applications, and

decision support. It has been difficult for public agencies to accurately assess largescale fire effects, and workshop participants agreed that future efforts in assessing fire

effects should focus on fire phenomena at large spatial scales. Because it is unlikely

that sufficient financial and human resources will be available to collect the information

needed to improve our ability to quantify the effects of fire, it will be more effective to

focus fire-science research and management activities on improving existing fireeffects models and linking them with other appropriate models.

This document contains a detailed articulation of critical issues-including specific

scientific and managerial questions and responses-relevant to the large-scale effects

of fire in North American ecosystems. The relative importance of these issues was

ranked by workshop participants in a structured format by using the analytic hierarchy

process, so that priorities are quantified both cardinally and ordinally. These rankings

provide the fire science community with a framework for guiding future research and

management activities on fire effects and can be reassessed periodically as new

information and models become available.
INTRODUCTION

From a human perspective, large and high-intensity wildland fires are one of the most

dramatic phenomena in nature. Although they are temporally infrequent, they have

large-scale spatial impacts: 1 percent of all wildland fires in the Western United States

may be responsible for as much as 98 percent of the land area burned (Strauss and

others 1989). Large fires are responsible for rapid changes in vegetation, soils,

biogeochemical cycling, microclimate, and many other ecological properties (fig. 1).

Fire is the most important periodic natural disturbance in most forest, shrubland, and

grassland ecosystems of western North America (Rogers 1996).

Although fire is known to play a critical role in the long-term dynamics of most ecosystems, there are many difficulties associated with scientific assessment and management of large-scale fire phenomena. This problem was brought into sharp focus in

1988 during and after the large fires in the Yellowstone National Park region. Although

paleoecological evidence indicates that fires of this magnitude (about 5000 square

kilometers total land area) had occurred previously in the region (Romme and Despain

1989), agency resource managers, administrators, and the general public seemed to

have limited awareness of the role of extreme fire events in Yellowstone ecosystems.

Our ability to understand and manage for the effects of large fires has been limited by

a lack of data for large spatial scales. There is a substantial scientific literature on the

effects of fire in terrestrial ecosystems (e.g., Wright and Bailey 1982), but the vast

majority of scientific data has been collected at scales of 10-l to 10 square kilometers

1
Fire Behavior

Long Term

Succession

Development

I

Aging

\ Spread rate \\

I

Ignition

Propagation Fire Event \ \

Historical

Conditions

Fuel

Weather /”

Climatic pattern

MOlStU~

\

I

Fire Behavior Effecta

Cover removal

l pattern

l degree

Consumption I

\

Heat penetration

Light absorption

Chemical changes I

Figure l-Large-scale fires have many effects and complex interactions. Figure from Paysen and others

1998.

(McKenzie and others 1996a). Extrapolating these data to fire phenomena at much

larger scales can result in substantial errors in estimating fire effects, because relevant

processes are different at different spatial scales (Simard 1991; table 1). The potential

for substantial errors when extrapolating fire effects across spatial scales is particularly

relevant for modeling fire and ecosystem processes.

Simulation models have proven to be useful tools for predicting the effects of largescale disturbance on ecosystems. Modeling is a convenient and practical alternative

to the expensive and time-consuming collection of large amounts of data at large

spatial scales. Models used to predict the effects of fire on vegetation can be grouped

in three categories (McKenzie and others 1996a): (1) stand-level mechanistic and

probabilistic fire behavior models, and first-order fire-effects models; (2) stand-level

successional models incorporating fire stochastically; and (3) landscape-level models

of disturbance. These models operate on different spatial and temporal scales,

although output from the first two types of models often is aggregated to larger scales.

Extrapolating ecological effects of fire across spatial scales can result in many sources

of error, including (1) directly extrapolating fire behavior models to larger spatial scales;

(2) integrating fire behavior and fire-effects models with successional models at the

stand level, then extrapolating upward; and (3) aggregating model inputs to the scale

I 2
Table l-General classification of scales and examples of relevant fire characteristics, processes, and influences for each scale

Scale classification

Micro

Mechanical

Sensory

Fire characteristics, processes, and influences

Energy flux, pyrolosis, personal attitude

Temperature, radiation, ignition, individual behavior

Weather observation, fire behavior, suppression, human

activity

Meso Thunderstorm, fire danger, dispatch, supervision

Synoptic Cold front, fire severity, mobilization, production

Strategic Drought, fire season, fire planning, organizational budget

Macro Climate, fire ecology, fire policy, government

Global Climatic change, fire history, treaty

Source: Simard (1991). Reprinted with permission of the International Association of Wildland Fire.

of interest. Regardless of which approach is used, extreme fire events pose a major

problem for modelers owing to the problem of propagating and compounding errors

across spatial scales. The challenge is to develop or adapt models that are scientifically sound as well as applicable to resource management issues.

Spatial and temporal variation in fire disturbance in the Pacific Northwest varies widely

by longitude, latitude, altitude, and ecosystem type (Agee 1990, 1993), thereby

providing a broad range of conditions for model development and testing. This region-generally considered to include Washington, Oregon, northern California, and

southern British Columbia-contains a broad range of climatic conditions, geomorphic

features, and elevations. This diversity of environmental characteristics is associated

with many types of ecosystems, including temperate rain forest, alpine meadows,

east-side pine forest, and semiarid shrub-steppe.

The diversity of environmental conditions and ecosystems in the Pacific Northwest

produces several fire regimes, which can be defined by characteristics of the disturbance (fig. 2) characteristics of the vegetation, or fire severity (fig. 3) (Agee 1993).

With respect to fire-severity classification, high-severity fire regimes have infrequent

fires (greater than 100 years between typically high-intensity fires) that often kill most

trees in a forest stand (Agee 1990). Moderate-severity fire regimes have infrequent

fires (25-100 years) that are often partial stand-replacement fires and include areas of

high and low severity. Low-severity fire regimes have frequent fires (l-25 years) that

are normally low-intensity fires with minimal impacts on forest overstories. Fires in

grassland and shrubland ecosystems tend to be in low- and moderate-fire severity

regimes in frequency but with rapidly moving, high-intensity fires.

3
Moisture stress index

Western

hemlocld

Douglas-fir --

Moisture stress index

Figure 2-Fire regimes in Pacific Northwest

vegetation types can be defined by physical

characteristics of the disturbance (O=little

fire influence, l-infrequent light surface fire

[>25 yr], 2= frequent light surface fire [l-25

yr], 3=infrequent severe surface fire I> 25

yr], 4= short return interval crown fire and

severe surface fire [25-100 yr], 5=long

return interval crown fire and severe surface

fire [loo-300 yr], 6=very long return interval

crown fire and severe surface fire [s300 yr]).

From Agee (1993; granted with permission

from “Fire Ecology of Pacific Northwest

Forests,” Agee, Qlsland Press, Aug.13,

1998. Published by Island Press,

Washington, DC, and Covelo, CA).

Figure 3-Fire regimes in the Pacific

Northwest can be defined by fire severity.

Stands in low-severity fire regimes have ~20

percent of basal area removed by fire, and

stands in high-severity fire regimes have

~70 percent basal area removed. From

Agee (1993), reproduced by permission,

Island Press.

Recent large fires (over 800 square kilometers of land area in 1994) in forest ecosystems

on the east side of the Cascade Range have posed a number of ecological, managerial,

and political problems. Current forest management practices and fire exclusion (active

through suppression, passive through alteration of fuel patterns by humans) may have

facilitated these large fires, pushing the fire regime from low severity to moderate or

high severity. In addition, age-class and fire-scar data indicate that infrequent, very

large fires on the west side of the Cascade Range and in the Olympic Mountains have

burned more than 10 000 kilometers in some years (Henderson and others 1989).

The stochastic nature of these events and the large spatial scales at which they occur
have proven difficult for scientists to analyze and public agencies to manage. Perhaps

appropriate vegetation and fuels management can mitigate fire severity or restore fire

regimes that existed during the past few centuries, but the large spatial scales of

extreme fires complicate postfire assessments and modeling efforts.

Given the complexity of large-fire phenomena, how do we improve our current scientific assessment and management of natural resources with respect to fire disturbance? How do we deal with a wide range of fire regimes in the ecologically diverse

Pacific Northwest? We cannot afford to wait for decades for the data and techniques

to improve our understanding and managerial approaches to fire disturbance in

ecosystems. We need to establish priorities now to optimize research programs,

develop resource management strategies, and encourage cooperation between

scientists and managers in the years ahead.

On 17-19 April 1996, a group of scientists and resource managers gathered for a firedisturbance workshop at the University of Washington to discuss these issues. The

objectives of the workshop were to (1) identify the current state of knowledge for fire

effects at large spatial scales; (2) develop priorities for a scientific approach to modeling large-scale fire disturbance and its effects; and (3) develop priorities for assisting

scientifically based decisionmaking for fire disturbance in resource management.

Although the focus was on the Pacific Northwest, issues of broader national and global

concern also were addressed. A structured workshop process was used to conduct

discussions, compile information, and elicit knowledge from participants. Our previous

experience with technical workshops (Peterson and others 1992, 1993; Schmoldt and

Peterson 1991) demonstrated that predetermined structure is important for achieving

useful workshop results.

We wanted to achieve a number of objectives, both strategic and tactical, during and

after the meeting. Strategic objectives for this workshop are listed above and in the

straw man document (a suggested framework or template, subject to revision, which is

used as a basis for discussion and analysis) (fig. 4). These objectives deal with the

overall accomplishments proposed for the workshop; i.e., describing, assessing,

prioritizing, and recommending large-scale fire-disturbance research and managerial

needs. A detailed tactical plan for achieving the strategic objectives also was developed; it is described briefly in Schmoldt and Peterson (1997). Tactical objectives for

the organization and conduct of the workshop were threefold:

l Content-To elicit expert judgment on large-scale fire disturbances that could be

used to guide future research and resource management efforts by the U.S. Depattment of Agriculture, Forest Service and cooperators, particularly in the Pacific

Northwest Region.

l Efficiency-To collect these judgments within a short time: 2 days.

l Product-To collect this expertise in a detailed and structured manner so that

results could be formulated into a publishable report (this paper) reflecting the

current state of knowledge about large-scale fire disturbance and future scientific

and managerial needs.

5
Figure 4-The straw man document was used to generate discussion by suggesting key questions and

responses for the four Workgroup topics. Workgroup participants had the option of using these questions

and responses, modifying them, or developing their own.

Links among fire effects, fuels and climate

What are the critical scientific issues regarding the impacts of fire on vegetation

and fuels?

l “Natural” and human-related conditions interact to affect both vegetation and

fuels. Natural factors tend to be stochastic. Human factors tend to be

planned, although consequences are not necessarily predictable.

l The long-term impact of changes in fire frequency on vegetation is poorly

quantified for most systems.

9 Landscape-level changes (e.g., ecosystem distribution) resulting from fire

frequency, size, and intensity are poorly understood.

l The short-term impact of changes in fire severity on vegetation is better known

for many systems.

What are the critical management issues regarding the impacts of fire on vegetation and fuels?

l Acceptable levels of impacts on vegetation and fuels need to be stated:

emissions, fire size, timber resource, watershed protection, exotic vegetation,

etc.

l Management objectives for vegetation composition and fuel loadings need to

be clearly stated.

l Long-term perspectives are needed for management of landscapes and

ecosystems.

What are the criticalpolMa/ issues regarding the impacts of fire on vegetation

and fuels?

Air quality: emissions must be restricted.

The role of prescribed burning as a management tool for modifying vegetation

and fuel loading should be assessed.

Social impacts (human safety and health, economic values) of prescribed

burning and wildfire need to be assessed.

Legal and logistic concerns with respect to political boundaries need to be

reconciled. Institutions need to cooperate as much as possible.

6
How can fhe relative impact of fuels and weather on fire regimes (frequency,

intensity, size, etc.) be quantified?

l The relative variability of weather and fuels needs to be quantified in a meaningful way. The relation of this variability to impacts on ecosystems must be

examined.

l The relative impact of fuels and weather will differ for different ecosystems.

l Historical fire data and climatic data need to be examined more rigorously in

different ecosystems. This can be done in conjunction with fire-behavior

modeling.

9 Fire-behavior modeling needs to be related to changes in landscape patterns

of vegetation and ecosystems.

Fire as a large-scale disturbance

What are the most important aspects of long-term changes in fire characteristics

on vegetation?

l Spatial patterns of vegetation distribution and abundance are sensitive to

changes in fire characteristics.

l Fire frequency, size, and intensity affect postfire vegetation composition.

l Fire frequency affects successional patterns for vegetation composition and

structure. The relative impact differs greatly among ecosystems.

l Fire size affects landscape patterns (e.g., patch size) and vegetation composition (through rate of vegetation establishment).

l Fire intensity affects postfire structure and regeneration.

l Fire occurrence in ecosystems previously having no fires can alter landscape

patterns and disrupt previous ecosystem structure and functional relations.

What is the current state-of-knowledge regarding the long-term interaction of fire,

vegetation, and climate?

l Fire frequency is affected by large-scale climatic patterns.

. Climate affects distribution and abundance of species on the landscape;

species composition of ecosystems is dynamic at large temporal scales.

l There is some evidence that large-scale changes in vegetation affect largescale climatic patterns.

l Climate affects the distribution and composition of fuels, which in turn affect

the size, frequency, and intensity of fires.

7
What aspects of fire as a landscape and ecosystem disturbance are relevant to

large-scale (spatial and temporal) modeling ? What aspects are particularly

relevant in the Pacific Northwest?

Fire induces changes in decomposition, biogeochemical cycling, and energy

cycling.

Impacts of large fires occur at very large scales. Systems are not in true

equilibrium, even over thousands of hectares and thousands of years.

Spatial patterns of vegetation distribution and abundance are sensitive to

changes in fire characteristics.

Fuel conditions are relevant at small and large spatial scales and change

temporally.

Weather data and conditions are normally relevant at large spatial scales.

Note, though, that weather and topography often interact at small spatial

scales. Because they impact fuels, and fuels are relevant at small scales,

weather can be relevant at small scales also.

Fire occurrence is stochastic but has a causal component (not random).

Events are often modeled as random (probabilistic) because we do not fully

understand, or cannot project, the underlying mechanisms.

Fire characteristics differ by latitude, longitude, and altitude (east side vs. west

side, northern vs. southern forest types, low elevation forest vs. subalpine).

West-side systems tend to have less frequent but larger fires than east-side

systems.

Pioneer species (e.g., alder) can rapidly alter vegetation distribution after fire.

Fire effects modeling structures

What existing models (or components) could be adapted or modified for proposed work by the Forest Service and cooperators? What modeling approaches

can be used with minimal collection of new data?

l FARSITE

l FIRESUM

l FEES

l FIRE-BGC

l LOKI

l MAPSS

. TEM
l Fire-behavior models

l General circulation models

l Need to consider whether steady state or transient modeling approach is appropriate.

l Need to clearly address transitions in vegetation types and fuel loading.

What are the relevant scale issues (spatial and temporal) related to modeling fire

impacts on vegetation and fuels?

l The appropriate scale of resolution needs to be determined for each modeling effort.

l Models need to be designed to minimize errors in extrapolation to larger

scales.

l Variation in vegetation and fuels and their response to fire may be different at

different scales.

l Most existing data on fire effects were collected and analyzed at smaller

spatial and temporal scales.

l Modeling needs to occur at one scale finer than the level of resolution desired

for projection or management decisionmaking.

l Effects over spatial distances can often be aggregated in obvious ways;

effects occurring over temporal distances often have no simple additive

property. Among other things, this means that these two types of scales

(spatial and temporal) need to be addressed very differently.

What are seine potential approaches for G/S-based modeling of fire impacts on

vegetation?

l Design models to take advantage of GIS databases.

l Examine one or more GIS databases containing evidence of large or frequent

fires. Search for patterns in different data layers.

l Link fire-behavior models to GIS databases (containing fuels information) to

generate landscape-level projections of vegetative changes resulting from fire.

How does one integrate climatic change scenarios in fire-vegetation modeling

(for scientific or managerial purposes)?

l Need to determine whether the steady-state or the transient modeling approach is more appropriate.

l A transient approach requires dynamic modeling of climate change; in particular, how fire-genie additions to atmospheric carbon and vegetative storage of

carbon affect climate.

l A straightforward approach is to identify climatic conditions and rates of

change for modeling purposes.

l It is important to understand and model the impact that climatic change has on fuels.
Managerial concerns, applications and decision support

How can a scientifically rigorous modeling approach be designed to be most

useful to resource managers? How should scientist-manager communication be

encouraged?

l Model logic should be sufficiently clear that managers can understand the

modeling process and provide input to it.

l Manager input and participation in model building will result in a better product.

l Regular exchange of information regarding modeling for a specific dataset

(e.g., a GIS vegetation database) may facilitate dialogue between scientists

and managers.

l Modeling should be adaptive; i.e., models should be continually revised as

monitoring data suggest revisions. Monitoring and model revisions will require

that managers and scientists work closely together.

What are the most useful model structures and outputs for resource managers,

decisionmakers, and policymakers?

l incorporating a probabilistic approach will provide a more realistic range of

output rather than a single “answer.” Note, though, that although probabilities

can be tracked, either rigorously or ad hoc, generating multiple scenarios for

particular inputs (as mentioned below) will be the most useful for managers.

By using the most likely array of input data, the most likely model output

scenario can be generated; likewise, less likely inputs will generate less likely

future scenarios. As time passes, it will be apparent which array is valid and,

therefore, which output scenario is likely to occur.

l Realistic and meaningful categories and classifications will be the most useful.

l Provide options for the model user that will allow for examination of realistic

alternatives for areas of uncertainty (e.g., a range of climatic conditions rather

than one assumed scenario).

How can decision-support systems assist resource managers with fire-effects

issues in planning and operations?

l Decision-support systems need to be straightfonnrard and accessible to

resource managers.

l Decision-support systems need to be integrated with GIS and other landscape-level tools.

l Resource managers need the capability to generate multiple fire-effects

scenarios based on different climatic projections.

10
l important thresholds in the modeling process and subsequent decisionmaking

can be identified.

l Critical features of modeling can be highlighted without the need for resource

managers to participate fully in the modeling process; they can then specialize

in management and decisionmaking.

l Resource managers can use decision-support systems in conjunction with

expert opinion from scientists and other managers.

The organization and process of the workshop were designed with these tactical

objectives of content, efficiency, and product in mind. The decisionmaking and group

discussion protocols that were developed included three main parts: (1) assign

attendees into discrete workgroups, which were the foci for workshop discussions;

(2) create a conceptual structure for organizing Workgroup discussion, a context for

the discussion content; and (3) develop a seven-step process for Workgroup conduct

to streamline identifying, assessing, prioritizing, and recommending research and

managerial needs. Workshop discussion centered around four broad content areas, or

primary topics: (1) links among fire effects, fuels, and climate; (2) fire as a large-scale

disturbance; (3) fire-effects modeling structures; and (4) managerial concerns, applications, and decision support. Because these topics are relatively disjoint and workshop

attendees possessed very specialized knowledge of them, we opted for small working

groups rather than one large session. Each Workgroup consisted of four to seven

members, dealt with a single fire topic, and had a discussion leader and a recorder.

Members of each Workgroup were given considerable freedom to move about and

participate in other workgroups as appropriate.

Each Workgroup was instructed to develop key questions for their assigned topic. For

each key question, they were asked to provide corresponding responses. Workgroups

also were asked to prioritize their list of key questions and, separately, their lists of

responses within each question. Priorities were assigned for both importance and

feasibility (or practicality). The analytic hierarchy process (Saaty 1980, 1990) was

used within this group setting to arrive at priorities. This conceptual structure is shown

in figure 5. After the workshop, statistical analyses were performed to determine which

key questions (and which responses within each key question) differed significantly in

priority. Lists of key questions, responses, and their priorities for importance and

feasibility were used to form recommendations regarding large-scale fire-disturbance

modeling. Because this document records fire workshop results, and not methodology, we do not elaborate further on details of the workshop’s conceptual structure and process. Readers are referred to Schmoldt and Peterson (1997) for

specific methodology.

1 1
Primary topic

Scope

resource. watershed

protection. exotic

Response rankings

*

* : #

Rankings

lmwrian Practicality

l o

- 0.00 - 0.00

- 0.00 - 0.00

- 0.w - 0.00

Figure 5-An illustration of the hierarchical structure of the straw man document for a portion of one primary

topic, including key questions, scope, and example responses for that key question. Workgroup responses

to key questions identify important issues and their practicality, which then enable us to recommend and

prioritize research projects. All key questions were assessed similarly.

12
Workgroups met for discussions on one day, plus two hours on another day. On the

third day, a member from each Workgroup made a summary presentation to the

plenary session. This allowed other attendees of the workshop to ask questions or to

offer suggestions. It was felt that constructive, intergroup feedback of this sort would

enable each group to further improve their analyses and final report.

The following four sections describe issues addressed and results produced in

Workgroup discussions. Despite the overall conceptual structure provided for the

workgroups, each topic differs in difficulty, current knowledge, and available information. These differences dictated adjustments to the discussion process to fit specific

needs. Consequently, the report of each Workgroup differs in style, level of detail, and

extent.

1 3

LINKS AMONG FIRE EFFECTS,

FUELS, AND CLIMATE’

Key Questions and This Workgroup developed five questions important to understanding the links among

Responses fire, fuels, and climate. These key questions are presented below in descending order

of importance. For each question, the group generated some general statements

about the subject matter of the question to establish a context for response discussions. The group then produced a set of responses to each question to define current

research and management needs. Each response was rated by the group on a final

scale of 0 to 1 (using the methodology presented in Schmoldt and Peterson 1997) as

to its (1) importance to management and research and (2) its “practicality” as defined

by the probability of successfully researching the problem and implementing a solution. In addition, each key question posed by the group received a rating of importance. Practicality was not considered for the key questions because the Workgroup

felt that the breadth of the key questions made such a comparison extremely difficult.

A summary of importance and practicality ratings for the key questions and responses

appears in table 2.

1 Workgroup members who developed this section: James Agee,

Larry Bradshaw, Sheri Gutsell, Emily Heyerdahl (Recorder), Robert

Keane (Leader), Ken Lertzman, and Kevin McKelvey.

1 5
Many factors other than fuels and climate affect fire. The Workgroup limited its discussion to fuels and climate, however, because it believed they are the most important;

moreover, to include in this discussion all processes affecting fire and its subsequent

effects would be unmanageable. The first key question therefore was composed to

identify the causal mechanisms that are an important link to fire and fire effects. This

question sets the stage for all remaining questions.

Table 2-Links among fire effects, fuels, and climate key questions and their

responses rated by importance and practicality

Key questions and responsesa Importance Practicalityb

1. What, where, and when are the following

factors important to fire disturbance?

Climate

Fire

Fuels

Biota

Physiography

Humans

2. What knowledge do we have about these links?

We must know fire severity, intensity, seasonality, and

pattern to understand links and interactions

Large-scale climatic events (synoptic) have a known

frequency and fire effect

Paleoclimatic and current climatic records are available

and can be used with simulation models to extrapolate

weather data

Preserve and analyze disturbance records on the landscape

A wide variety of fire history data exist and can be valuable

&& are much more variable (in time and space) than

climate and their impact differs with fire severity

Intensity and severity of fire are very different; severity is

related to fire effects, and intensity is related to behavior

Fire propaoation processes are important to link with

other ecosystem processes

Fire ignition has numerous sources and depends on fuel

bed and moisture

0.38 -

0.25 -

0.16 0.07

0.15 0.12

0.14 0.16

0.13 0.08

0.10 0.16

0.08

0.08

0.08

0.07

0.09

0.11

0.09

0.12
Table P-Links among fire effects, fuels, and climate key questions and their

responses rated by importance and practicality (continued)

Key questions and responses8 Importance Practicalityb

3. At what scales are processes important?

Propagation of errors must be accounted for across scales

An ecoloaical data structure spanning many scales is needed

A scale of analysis (e.g., landscabe scale) must be defined

to integrate coarse- and fine-scale processes

Multiole scales should be incorporated in simulation

approaches

ExDlanatory coarse-scale models are needed to refine the

predictive ability of other models

A cross-scale decision-support tool is needed for managing

wildland and prescribed fire

Fire characteristics must be intimately linked to weather

and climatic processes

4. How are links related in a IandscaDe context?

Landscapes need to be enaineered to lie within acceptable

limits of fire behavior and severity and still function as

an ecosystem

A method is needed for evaluating the effectiveness of

vegetation- and fuel-manaaement strategies at the

landscape level

A better understanding is needed of the influence of

linked orocesses to landscape structure, composition,

and function and vice versa

Need to predict fire regime from the other ecosystem

processes

Landscape representations and analysis procedures are

needed that are useful to both research and management

A better understanding is needed of how the adjacency of

vegetation patches affects and is affected by heat from fires

A better understanding is needed of the dynamics of

fire breaks spatially and temporally

0.17 -

0.20 0.15

0.16 0.08

0.14 0.15

0.13 0.09

0.13 0.19

0.12 0.07

0.11 0.26

0.11 -

0.28 0.14

0.21 0.15

0.15 0.15

0.12 0.09

0.10 0.09

0.08 0.25

0.07 0.14

1 7
1 8

r--

Table 2-Links among fire effects, fuels, and climate key questions and their

responses rated by importance and practicality (continued)

Key questions and responses8 Importance Practicalityb

5. What links have a high level of manaaement importance?

The public needs to be encouraged to be actively involved

in decisionmaking in ecosystem management

Scientists must provide a summary of current

knowledge to manaaement

A severitv measurement (with units) is needed that

integrates frequency, variability, intensity duration,

season, and synergistic effects of fire

A system is needed to predict which processes enable

fire events (risk) as they interact in both time and space

Fire reaimes must be described quantitatively in terms of

severity and intensity

Better predictions are needed of biotic resoonses as fire

and climatic processes change

Technoloay is needed to manage large-scale events

A system is needed to predict emissions from fire

A better understanding is needed of the interaction of

these processes on smoke production

0.08 -

0.17 0.21

0.17 0.26

0.12 0.07

0.12 0.06

0.11 0.05

0.10 0.07

0.08 0.08

0.07 0.12

0.06 0.10

“Underlined words are used as shorthand notation in analyses in tables 6-14, p. 66-70.

bThe Workgroup did not compare key questions with respect to practicality. They also felt that the responses

to key question 1 were too interrelated for comparisons to be made.

Question 1: What, where, and when are fuels and climate important to fire

disturbance?

The first important caveat is that it is the interactions of these factors that are important

to describing fire, not simply the factors and processes taken individually. A comprehensive discussion of individual processes is helpful to understand the context of the

fire environment, but it is how these processes interact that truly dictates fire dynamics.

Because of dependencies among these factors, the group decided not to generate

importance or practicality ratings for the responses to this first question. Instead,

these factors were used as background for all other questions.

The first part of the question ‘What are the important factors?” was discussed in detail,

and it was decided that the following list would generally describe those processes

important to fire, especially at the broad (or coarse) scales:

l Climate: Controls extreme events, particularly where there are significant fuel

loadings, and is a broad-scale process. Synoptic-scale weather patterns affect mid

to fine scales, including surface temperature, precipitation, and fuel moistures
(Balling and others 1992; Bessie and Johnson 1995; Brenner 1991; Clark 1990a,

1990b; Johnson 1992; Johnson and Wowchuk 1993; Vasquez and Moreno 1993;

Wein and MacLean 1983).

. Fire, behavior and effects: Wildland fire is the process that shapes landscapes and

dictates species compositions (Albini 1978; Anderson 1969; Byram 1959; Crutzen

and Goldammer 1993; Heinselman 1981; Johnson 1979,1992; Johnson and

Larsen 1991; Johnson and Van Wagner 1985; Masters 1990).

l Fuels: Both dead and live organic matter contribute to the combustion process.

Fuels include both living and dead vegetation and are highly influenced by vegetation structure. Fuels control fire when weather is not extreme (Brown and Bevins

1986, Brown and See 1981, Frandsen and Andrews 1979, Spies and others 1988,

van Wagtendonk 1972, Williams and Rothermel 1992).

l Biota: All living things in the fire environment comprise the biota. The type of

vegetation affects live and dead fuel characteristics and the nature of fire behavior.

Fire effects range widely depending on resistance of organisms to fire and growth

and regeneration after fire (Agee 1993; Bond and van Wilgen 1996; Goldammer

and Jenkins 1990; Johnson 1979, 1992; Prentice and others 1993; Wright and

Bailey 1982).

l Physiography: Slope, aspect, landform, slope shape, slope position, and elevation

define physiography. Topography directly influences the orientation of the fuel bed

and indirectly controls landscape composition and structure (Albini 1976, Andrews

1986, Fensham 1990, Rothermell972, Swanson and others 1990).

l Humans: Land-use and land management often influence fire and ecosystem

dynamics on the landscape (Pyne 1982,1984).

The Workgroup developed several general statements addressing spatial and temporal

aspects of fire processes.

l Extreme fire events currently burn the most area. Only about 1 percent of fires burn

over 98 percent of the involved land area (Johnson and Wowchuk 1993, Strauss

and others 1989).

l Extreme fire events are controlled by climate (Bessie and Johnson 1995, Johnson

1992, Johnson and Wowchuk 1993). Extended drought is the primary factor

responsible for severe fire seasons. Extreme fire events burning during droughts

are usually wind driven and are of such high intensity that the other factors listed

above have an insignificant effect on fire behavior.

l Fuels, topography, weather, humans, and the biota are the major factors influencing

fire dynamics in nonextreme years.

l Fire behavior in the nonextreme years affects heterogeneity in landscape composition, pattern, and structure (Arno and others 1993, Forman 1995, Forman and

Godron 1986, Marsden 1983, Pickett and White 1985, Turner 1989, Turner and

Gardner 1991, Turner and Romme 1994).

1 9
Climate

After these statements were made, it was evident that research could provide important information needed by resource managers. The following is a small set of needs

statements generated by the Workgroup:

l Need to identify and predict the conditions of those factors enabling extreme

(severe) fire events.

l Need to understand and integrate the role of all factors and processes in fire

dynamics.

l Need to compute the probability of large-scale disturbance events and evaluate risk.

After careful deliberation, the Workgroup decided on a set of questions that would not

be discussed because of the short discussion time. These are important questions

that research must investigate but the Workgroup could not address in detail:

l What additional human-oriented factors influence these links? More specifically,

how do society, politics, and culture influence processes and interactions in the fire

environment?

l What was the role of native peoples and their interactions with fire process links?

Did native peoples change the pattern of fire or complement existing patterns?

Question 2: What do we know about these links?

The Workgroup assessed existing knowledge about processes affecting coarse-scale

fire dynamics to identify possible research areas. This knowledge base includes

literature, models, databases, spatial data layers, and expert systems. Responses

were stratified by individual fire-related processes, recognizing that interactions are

important. Some broad statements were developed to provide a context for an inventory of fire-process knowledge.

Large-scale synoptic events have a quantifiable historic frequency and fire effect

for recent periods (post-1940) (Arno and others 1995, Barrett and others 1991,

Heinselman 1973, Johnson 1979, Johnson and others 1990, Johnson and Larsen

1991, Masters 1990, Reed 1994). These climatic events include mid-tropical anomalies (Johnson 1992, Johnson and Wowchuk 1993) and El Niiio-Southern Oscillation

(ENSO) events (Brenner 1991, Swetnam and Betancourt 1990). There is subcontinental variability in the timing and magnitude of major climatic events (Clark 1990a,

Clark and others 1996, Johnson 1992). The extremes of these events, either very wet

or very dry periods, dominate the fire environment. Between the extremes, short-term

weather, fuels, and topography have a stronger influence on the fire environment. At

some point, the fire-environment dependency switches from fuels-weather-topography

to climate (after a long period of hot, dry weather) and “enables” landscapes to burn

regardless of composition, structure, and pattern; however, this threshold of change is

unknown. We also know that when an ecosystem is in an “enabled” state, large-scale

disturbance may not occur as a result of other factors, such as lack of ignitions and

wind. Large-fire years are important because large fires burn most of the total area

burned, and these fires typically are the most severe and intense.

2 0
Current knowledge of climate and climatic dataLong-term climatic record-General trends can be inferred from paleoclimatic

records, such as packrat middens, pollen records, charcoal, ice cores (<lO,OOO years),

tree cores (~4,000 years), and sunspot records (Allison and others 1986, Arens 1990,

Gajewski 1987, Hopkins and others 1993, Singh and others 1981, Swain 1973,

Swetnam and Baisan 1996). These data sources may be loosely correlated to largescale disturbance patterns.

Current climatic records-These data are reliable but limited in spatial and temporal

scale (circa 1900 to present). Most data have maximum and minimum temperatures and

precipitation. Data quality and length of record are highly variable. Sources include

U.S. National Weather Service Climatic Data Center and Canadian Atmospheric

Environment Service (50 to 100 years B.P.), U.S. Natural Resources Conservation

Service (SNOTEL) (1980 to present), and U.S. Department of Agriculture (USDA),

Forest Service and Bureau of Land Management (BLM) fire weather networks.

Simulation models such as MT&LIM (Hungerford and others 1989), PRISM (Daly and

others 1994), and DAYMET (Thornton and others 1997) are useful for extrapolating

weather data from base stations across mountainous terrain. Continuous spatial data

layers can be constructed for any number of time intervals and areas.

Climate mode/e-These include general circulation models such as UKMO

(Schlesinger and Mitchell 1987) and GISS (Hansen and others 1988) with mechanistic regional-scale models. These models will be increasingly useful, but there are no

known long-term stochastic or empirical models for spot-weather forecasts (Fosberg

and others 1993, Shands and Hoffman 1987). Global-scale models probably do not

contain sufficient detail to accurately capture or define the establishment of “enabled”

states of risk, although research efforts are underway to develop finer spatial-scale

resolution weather predictions from general circulation model output.

Fuels Of the six fire factors listed in question 1, above, we know the least about fuel dynamics. It is generally accepted that fuels are highly variable in time and space. Fuels are

very important in small- and moderate-scale fires but less important for extreme fires

(Bessie and Johnson 1995). Fuel loadings are more dependent on vegetation than

weather in the short term, but in the long term, it is climate that ultimately dictates the

rates and magnitudes of fuel dynamics (for example, fuel moisture, decomposition).

Most fuel studies substitute space for time in the sampling scheme rather than use

permanent plot remeasurements. This results in both across- and within-site errors.

Probably the most important fuel characteristics affecting fire dynamics are bulk

density, loading, surface area-to-volume ratio, vertical and horizontal continuity,

moisture content, and live-versus-dead fraction (Brown 1981, Brown and Bevins

1986). The most important fuel variables affecting ecosystem dynamics are probably

loading, coarse woody debris (size, length, rot), duff depth and distribution, snag

density, moisture content, and particle distribution.

21
Fire

2 2

Links between fire and fuels are different than links of fuels to other ecological processes. Many ecological processes and ecosystem characteristics are strongly

influenced by very large fuels. Moisture retention in these large particles is controlled

mainly by saturation during rainy periods or in winter. Fire, on the other hand, is

strongly affected by quantities of fine fuels and their moisture contents, which differ

day-to-day with atmospheric humidity. Under low to moderate fire weather conditions,

large, ecologically important elements often will be only partially consumed by fire. I n

extreme drought conditions, however, these large logs burn over long periods under

smoldering and direct combustion processes. Long fire-residence times, even if fire

intensity is not extreme, can cause root and cambium mortality and contribute to plant

mortality (Peterson and Ryan 1986, Ryan and Reinhardt 1988) and changes in soil

properties (Albini and others 1996, Wells and others 1979).

Available temporal fuels data-Few studies of temporal variation of fuels in the

United States have permanent plots. Some that do include the Sierra Nevada,

Yosemite National Park (7 years and ongoing), Yellowstone National Park (Renkin and

Despain 1992), western Cascades (Spies and others 1988), western Montana, (5 years

and ongoing; Keane and others 1996b), Coconino National Forest (Arizona, 20 years

and ongoing), Francis Marion National Forest (South Carolina, 30 years and ongoing),

and Appalachicola National Forest (Florida, 30 years and ongoing).

Available spatial fuels data-Most fuels inventories have substituted space for time

in their sampling approach, and these studies usually are stand-based approaches.

Examples are:

l Fuel descriptions, photo series (Fischer 1981)

l Fuel databases (Brown and See 1981, Jeske and Bevins 1976)

l Sierra Nevada Ecosystem Project (SNEP 1996)

l Montana and Idaho, gradient remote sensing study (Keane and others 1996b)

l Fuels maps or geographic information system (GIS) layers (Hardy and others,

in press)

l EROS fuel map (Hardy and others, in press; Loveland and others 1991)

l Future fuels (photo series under development by Forest Service Research Stations)

l Simulation models: many mechanistic vegetation models can be used to simulate

fuel dynamics; for example Keane and others (1989,1996c) and also see Shugart

and West (1980).

Fire is the primary disturbance process in most North American ecosystems. There is

an important difference between fire intensity and fire severity: fire severity is related

to fire effects and describes the influence of a fire on the biota, whereas fire intensity is

related to fire behavior and describes the physical characteristics of the fire. We must

know fire severity, intensity, seasonality, and pattern to understand the links and

interactions in fire dynamics. The most important fire-behavior characteristics are

listed below with their unit of measure where appropriate. The variability of fire intervals may have a major effect on the character of the vegetation.
l Fuel consumption (kilograms per square meter)

l Rate of spread, intensity (meters per second, kilowatts per meter)

l Duration (smoldering vs. direct combustion)

l Size and pattern (hectares)

l Soil heat pulse (degrees Celsius)

l Frequency (per year) and its variability

l Surface versus crown fire

l Smoke and emissions (kilograms per hectare)

l Propagation processes

l Spot-fire mechanisms

l Ignition dynamics (sources, fuel bed, moisture)

Fire models-Several spatial and nonspatial fire models are available. Among them

are BEHAVE (Andrews 1986) FARSITE (Finney 1994, 1995; Finney and Ryan 1995)

Canadian Fire Behavior Prediction System (van Wagner 1987) and cellular automata

models (Clark and others 1994). All, however, have some limitations, including

restricted mostly to modeling of surface fires, no link to fire effects, scale dependent,

require specific fuels and forest structure, difficult to field test and validate, assume

homogenous fuel conditions and adequate definition of the entire “cell,” simulated

burns lack islands of unburned vegetation, and incorporate a limited number of spot

fires.

Fire-effects models-A limited number of fire-effects models exist. They include

CONSUME (Ottmar and others 1992), FOFEM (Keane and others 1994, Reinhardt

and others 1996) empirical equations (Brown and others 1985), mechanistic models

(Peterson and Ryan 1986), BURNOUT (Albini and Reinhardt 1995, Albini and others

1995), smoke dispersion models (PUFF, CALPUFF, EPM; Harrison 1996) and soil

heat-pulse models (Albini and others 1996). These models have some of the same

limitations of the fire-behavior models, including limited scope (geographical, ecological, vegetation), limited focus on vegetation and fuels, high variation in reliability, scale

dependency, difficulty in field testing and validation, and assumed homogeneous forest

conditions and stand-wide burns.

Emission-production and smoke-dispersion models-Emission-production and

smoke-dispersion models do not have the same limitations as other fire-effects models. Emission-production models (e.g., EPM; Sandberg and Peterson 1984) and

smoke-dispersion models (e.g., NFSpuff, CALPUFF, SASEM, TSARS+, and VSMOKE;

Breyfogle and Ferguson 1996), consider topography and atmospheric conditions and

require results from fuel consumption models as inputs. They are too difficult to test

and validate but are designed for a broad scope of applications and varying spatial

and temporal scales.

23
Historical fire records-More than 300 fire-history studies have been done in the

United States and Canada since the 1940s. Most have been in the Western United

States and Canada. These fire-history studies have characterized fire frequency quite

well, but few have investigated the spatial extent of fires. Most studies have been in

dry, low-elevation vegetation types having the most fire scars and where fire is relatively frequent. Subalpine and alpine environments have not been studied as often,

and fire-history records often are incomplete. There also are some methodological

problems with study designs that may reduce the spatial scale of inferences.

Fire-history studies have been very successful in the last 50 years in quantifying the

frequency, severity, and extent of wildland fires in forested ecosystems (Arno and

others 1993, Baker 1989, Barrett and others 1991, Foster 1983, Johnson 1979,

Johnson and Larsen 1991, Johnson and others 1990, Masters 1990, Swetnam and

Baisan 1996). Three primary methods seem to be used to measure recent fire histories.

Charcoal sediments in varve lakes provide a general description of fire frequency.

Dating fire scars on tree and shrub stems probably provides the most accurate method

of quantifying fire frequency (Johnson and Gutsell 1994). These are point records,

however, and do not always accurately describe the extent and severity of fire. Tree

and shrub age distributions can be used to date the last fire in a stand, and if all stands

are dated, then the extent and possible severity of fire can be assessed (Johnson and

Gutsell 1994, Yarie 1981).

It is critical to preserve, sample, and analyze fire-disturbance records on the landscape. This means that a sincere effort must be made to identify, locate, measure,

and analyze landscapes containing disturbance records, such as fire scars and forest

stand development data. Field data, such as fire scars, in particular on stumps, will

disappear after wild fires and prescribed fires.

Archival documents-Many sources of historical fire records may be used to characterize and study wildland fire. The U.S. General Land Office has archival documents

of land-survey data that may be useful to describe vegetation composition and structure (Habeck 1994). The Forest Service and BLM have fire reports complete for most

fires since about 1970. Many Forest Service and BLM districts have hand-drawn fire

atlases that coarsely define fire boundaries. These records have some serious

limitations, however: first, they are not consistently reported across agencies and

geographical areas; second, most are not accurately defined spatially or are temporally inaccurate; and third, many of these documents are difficult to obtain, read, and

enter into a standardized database or georeferenced database.

Photographic chronosequences-Past photo sequences provide a qualitative

description of fire severity and extent (Gruel1 1983, 1985). Photo series can be aerial

photos, ground-based photos (orthophotos), or satellite images. The major limitation

of these is that fire-regime characteristics cannot be measured. Landscape pattern

can be delineated, but fire frequency cannot be described quantitatively without

ground sampling. High-severity fire regimes are better analyzed this way than are

low-severity regimes with more uniform forest canopies.

2 4
Historical forest maps-These maps and GIS layers contain some representation of

age and size class structure such that the year of the disturbance event that created

the stand can be estimated. Unfortunately, many of these maps are inconsistent,

inaccurate, and often inappropriate for fire-history dating. They probably are appropriate only for crown-fire regimes, because regime ages are often based on heights, and

the maps often assume fire is the only disturbance. In addition, small polygons often

are missed.

Timber and range inventories-Each agency performs an inventory of its own lands;

however, these inventories are not comprehensive for fire applications, because they

are geared toward resource quantification rather than fire size and date. Additionally,

they contain mostly descriptive information on fire. Forest inventory analysis plots

established by the Forest Service and other agencies may be an important source of

temporal tree dynamics.

Anecdotal accounts-Although unquantified observations may be the only available

information in some cases, these sources are subjective and often inaccurate.

Bog and lake cores-Fire frequency estimates from cores taken from lake sediments

are coarse-scale descriptions of fire occurrence (Clark 1988a,1988b; Clark and others

1996), but the estimates are useful only for identifying certain periods when large fires

burned in close proximity to the area sampled (Clark and others 1989). Interpretation

of these cores is limited in time and space because cores can include a period that

may be as many as 4,000 to 8,000 years B.P. These estimates are from point

sources, and it is difficult to make any generalizations about the spatial frequency and

extent of fire in surrounding areas. ’

Current fire records-Many government agencies are required to record some

coarse descriptions of fires and their effects, and although some of these records are

now in standard formats, there is relatively little information on fire effects at large

spatial scales. Fire atlases are available at many Forest Service and BLM district

off ices.

Biota The biota includes all living things comprising an ecosystem. Genetic variability of the

biota will be important as climates and fire regimes change. Species and plant responses to climate and fire-regime change are individualistic and occur mostly during

the establishment stage. Rates of species change will be more directly related to

changes in fire regime than will direct species-climate interactions. Indeed, fire

creates conditions accelerating the change in species composition as it relates to

climatic change. Patterns on the landscape will dictate adaptations, distribution, and

migration of species. Landscape changes will be rapid at first, then will slow as fire,

biota, and climatic conditions equilibrate. Landscape-biota response to fire and

climatic change will be less dramatic than stand-level responses, however. Future

climatic and fire regimes will create some unique plant assemblages, perhaps even

create communities that never occurred historically. Generalist species will initially

predominate on future landscapes (Flannigan and van Wagner 1991, Shands and

Hoff man 1987).

25
Simulation models-Many models simulate successional dynamics. These models

are empirical, stochastic, process-based, or mechanistic (Shugart and West 1980).

Most vegetation dynamics models are stand based, but several landscape-level,

spatially explicit models have been developed. Probably the most commonly used

vegetation models are the gap-phase models first pioneered by Botkin (1993) with the

JABOWA model. Among the models that include fire dynamics are FIRE-BGC (Keane

and others 1989) SILVA (Keane and others 1996c), and FIRESUM (Kercher and

Axelrod 1984). Other models include SIMFOR (habitat supply model), DISPATCH

(Baker 1993), CRBSUM and LANDSUM (Keane and others 1996a), VDDT, and FVS

(formerly PROGNOSIS; Wykoff and others 1982).

Conceptual models-Many conceptual and diagrammatic models are available that

simplify the succession process. Most notable is the multiple-pathway approach of

Noble and Slatyer (1977) and Cattelino and others (1979). Kessell and Fischer (1981)

integrate these concepts into a management-oriented model. Kessell’s (1979) gradient model also describes and quantifies the successional gradient and correlates this

gradient with environmental conditions. Fischer and Bradley (1987) use these concepts for a simplified midscale succession model. Arno and others (1985) and Steele

and Geier-Hayes (1989) integrate the successional “pyramid” concept developed by

Hironaka (1989) into a management-oriented classification of successional community

types in a habitat type. See Bond and van Wilgen (1996) for additional conceptual

fire-succession models.

Expert systems and artificial intelligence-There has been a recent explosion of

vegetation models based on expert systems and artificial intelligence (Al), in which

parts of the above models are incorporated in their architecture. Chew’s (in press)

model, SIMPLLE, is a good example of a successful Al application of succession

modeling. Also, the fire effects information system (Fischer and others 1996) includes

successional information with an inference engine.

Databases-Most land management agencies, the Forest Service in particular, have

extensive databases describing successional processes. Most databases have

substituted space for time in their sampling strategies so that high geographic and site

variabilities are inherent in the data. There are, however, some temporal data sets that

go back 30 to 50 years. Stickney (1985) has a comprehensive temporal successional

data set from western Montana.

Spatial data-Many sources of spatial data can be used to quantify succession. Finescale sources include historical and current land management plan maps (habitat

types, potential natural vegetation), aerial photos, and archived records. Coarse-scale

sources include Kijchler potential vegetation maps, Bailey’s ecoregions map, Society

of American Foresters maps, satellite imagery, Mission to Planet Earth satellite imagery products, and a host of other satellite and airborne platforms. The limitation of

most of these data is that they rarely go back more than 80 years, and in most cases,

the historical record goes back less than 20 years.

Successional classifications-Many studies have attempted to classify successional

development after fire. See the annotated bibliography by Elliot and others (1993).

26
Physiography

Autecological and synecological plant information-Abundant data on the response of plants to fire exist in the literature. Most are stored in the fire effects information system (Fischer and others 1996).

Physiography can be important in influencing fire dynamics, especially for smaller fire

events, but few quantitative data or tools are available for assessing this important

factor. Some studies have examined the effects of physiography on fire frequency and

found them insignificant (Johnson 1992, Johnson and Larsen 1991, Johnson and

others 1990, Masters 1990). Perhaps a more logical approach would be to identify

those physiographic entities that can be controlled or managed and include them in an

assessment of landscape thresholds. Physiographic effects are probably applicable

only to problems at small (up to a few hectares) to moderate scales (up to a few

square kilometers), and their descriptions should be pertinent only to the issues at

these scales.

Question 3: At what scales are processes important?

In questions 1 and 2, a context was provided for interpreting the relative importance of

research and management needs in understanding and managing fire and ecosystems. This and the next two questions attempt to describe a working structure in which

the research and management needs can be solved. The Workgroup generated a set

of responses to these questions that attempt to capture the important factors that

should be included in any research or management project.

l Error propagation must be accounted for across scales. Assessing the accuracy of

predictive models, spatial data layers, and collected field data is essential for land

management credibility. Innovative methods are needed to determine prediction

errors so that land managers can provide the public with important information for

interpreting land management treatments. Error characterization will hold researchers and management accountable for the tools and information used in management analysis.

l An ecological data structure spanning many scales is needed. Data sampling,

storage, and analysis structures hierarchically nested across temporal and spatial

scales are badly needed by most management agencies. These structures must be

scientifically based but directly applicable to management. Sampling methodologies must be developed to validate products derived from remote sensing and

relational databases. We need a ground-based sampling system that validates or

tests simulation models so that the degree of error can be estimated. This task

would be relatively difficult to accomplish.

l A scale of analysis must be defined to integrate coarse- and fine-scale processes.

The scale of analysis for research and management activity investigations must be

clearly defined. This scale of analysis can be spatially defined by a resolution level

(such as 1:250,000 map scale) and a minimum mapping unit (such as 30-meter

pixels). At the very least, the size of the analysis area needs to be triple the size of

the largest disturbance to properly and meaningfully portray landscape dynamics

and patchiness.

2 7
l Multiple scales should be incorporated in simulation approaches. Important processes must be assessed at appropriate scales. In addition, some “unimportant”

processes (e.g., species migration, local weather, genetic plasticity) can become

important as landscapes, fire, and climates change, so they should be incorporated

in any analysis. We will continue to need imagery and data products that span

many spatial and temporal scales.

l Explanatory coarse-scale models are needed to refine the predictive ability of other

models. Process-based (mechanistic) and empirical models must be used in

tandem for most management projects. Process-based models can be used to

refine, modify, and identify new sampling areas for empirical models. Mechanistic

relations that are difficult to quantify through conventional means can be evaluated

by using empirical techniques. Coupling empirical and mechanistic (and even

stochastic) models may allow a synergistic ecological application that is efficient,

cost-effective, and timely.

l A cross-scale decision-support tool is needed for managing wildland and prescribed

fire. Decision-support tools should include more than one scale of analysis (both

time and space), and these tools should be compatible with each other. These

decision-support tools should present fire managers with a synthesized summary of

all available scientific products and tools so that resources and people can be

managed effectively.

l Fire characteristics must be intimately linked to weather and climatic processes. A

system is needed that relates fire-season weather trends to fire extent, intensity,

and severity. Given that the most land area is burned during severe fire seasons,

tools must be developed to predict when these fire years might happen and to what

extent they can be managed. Weather and climatic scales must be included in this

tool. This task would be relatively easy to accomplish.

Question 4: How are links related in a landscape context?

l Landscapes need to be engineered to lie within acceptable limits of fire behavior

and severity and still function as an ecosystem. Tolerance limits or thresholds of

natural and management activities need to be established for individual landscapes,

and management activities should never violate established limits. In addition,

large-scale experimentation should be conducted to identify these thresholds so

that biological diversity is conserved. How do we preserve refugia (e.g., areas

where fire should be excluded to protect wildlife) and still remain within acceptable

thresholds? How many possible engineering solutions can one landscape have?

Can a set of alternatives be engineered?

l A method is needed for evaluating the effectiveness of vegetation- and fuel-management strategies at the landscape level. How is the relative success or failure of

a land management strategy assessed across many temporal and spatial scales?

Can a management action fail in the year after treatment but succeed after 10 to

100 years? Can a land management action causing unacceptable disturbance

consequences in one stand result in an overall improvement of conditions across

the landscape? A method or tool is needed that can prioritize areas in the greatest

need of vegetation and fuels management. This task would be relatively difficult.

28
l A better understanding is needed of the influence of linked processes to landscape

structure, composition, and function and vice versa. How do coarse-scale properties of fire, climate, and physiography affect the dynamics of landscape ecosystems? What is the level of “resilience, plasticity, and hardness” of a landscape

necessary to withstand, absorb, and incur disturbance, whether human caused

or natural? We need to define the roles of exotic plants, animals, and fungi in

ecosystems so that their impacts can be managed (Christensen 1990).

l Fire regime should be inferred from other ecosystem processes. Can, and should,

fire be reintroduced to some ecosystems without adversely affecting other ecological

processes? A method is needed to evaluate this approach for landscape planning.

The most appropriate fire regime must be introduced to ecosystems, and these

regimes must take into account changes in climate, vegetation, human development, and exotic invasions.

l Landscape representation and analysis procedures are needed that are useful to

both research and management. Statistical tools and indices are needed to assess,

compare, contrast, and evaluate various management alternatives at a landscape

level (Turner and Gardner 1991). Landscape metrics are needed that are useful for

describing disturbance and vegetation properties. These indices and programs

should be robust to spatial and temporal scale and incorporate management

attributes into their design.

l A better understanding is needed of how the adjacency of vegetation patches

affects and is affected by heat from fires. When do landscape patches act as fire

breaks and when do they act as fire enhancers? How do patch characteristics

affect coarse-scale properties as well as those ecosystem attributes that act across

scales (e.g., wildlife, species migration, and insect populations)? This task would

be relatively easy to accomplish but would require an accurate accounting of

contagion processes. Perhaps it can be done through an intensive analysis of firefrequency studies.

l A better understanding is needed of the dynamics of “fire breaks” spatially and

temporally. More information is needed on the roles of natural and human-caused

fire and fuels patterns and on the spatiotemporal conditions under which vegetation

would act as a fuel break or a carrier of fire. Data also are needed on how fuel

landscapes can be divided to limit or carry fire.

Question 5: What links are important to management?

l The public needs to be encouraged to be actively involved in decisionmaking for

ecosystem management. The success of ecosystem management (EM) will

depend greatly on the ability of the public to understand and accept this land

management philosophy. Everyone needs to understand the role of fire and its

effects in each EM plan. Terminology should be understandable to both the public

and professionals. Landscape changes and dynamics can be described such that

the public will relate to them (e.g., fishing, aesthetics, remoteness, jobs). The

integration of sound science with management practices should be explained in

detail to the public.

2 9
l Scientists must provide a summary of the state of knowledge to management.

Scientists can no longer provide only information, tools, and concepts for EM; they

also must provide the training, utility, and context for this knowledge. Researchers

must make their results available to management. But research also should

synthesize these results in a useful manner and provide for their interpretation in a

management context. In addition, researchers should strive to summarize research

results for the public as well as the resource professional.

l A physical measure of severity (with units) is needed that integrates frequency,

variability, intensity, duration, season, and synergistic effects of fire. This integration

would be difficult to implement. Perhaps an index or number need not incorporate

all the facets of fire regime, but it must be solidly based in physical science and

describe process interactions rather than state variables. This index would provide

a sense of validity to fire-effects measurements and predictions.

l A system is needed to predict which processes enable fire events (risk) as they

interact in both time and space. This is one of the most important issues facing

resource managers. Better approaches are needed for predicting where, when, and

how large-scale fire events occur. Specifically, a better method is needed for

predicting the physics, dynamics, and effects of crown fires (Rothermel 1991) for all

ecosystems across large land areas (such as a national scope).

l Fire regimes must be described quantitatively in terms of severity and intensity. Fire

potential should be characterized in terms of a landscape, not just a stand. This

potential must be described in terms of the effect it will have on the biota and should

be physically based. It is critical that EM projects have some quantification of fire

severity to give them credibility and validity.

l Better predictions are needed of biotic responses for a wide range of fire and

climatic conditions. Research must articulate, model, study and speculate, in

simple terms, how and why fire, fuels, climate, and the biota will change as land

management strategies are intensified and the climate gets warmer. This means a

better ecophysiological characterization will be needed for plants and animals in the

Pacific Northwest. A conceptual model must be developed that can be used to

approximate the response of all biota to climate, fire, and fuels changes. Management treatments must be developed that do not cause adverse impacts under new

climatic and management conditions.

A corollary to improved predictions is making them available to management and the

public. Perhaps an important issue is how fire affects postfire populations of insects,

fungi, mammals, and people, and how fire and these factors act together to increase

tree mortality. These tools should probably be mechanistically based so that they can

be expanded as climate, fire, and biota change. Genetic variability must be incorporated into model and tool parameters to account for genotypic shifts in species abundance.

l Technology is needed to manage large-scale events. All existing technology must

be integrated in a synergistic application that will allow us to manage severe and

large-scale fire events better and more efficiently. We can no longer afford to spend

large quantities of money suppressing fires.

3 0
Synopsis

l A system is needed to predict emissions from fire. To justify burning in ecosystems,

a comprehensive system must be developed that predicts smoke production,

dispersion, and health effects across many time and space scales. This system

must have a mechanistic approach and account for the combustion of fuels, liberation of combustion products to the atmosphere, and dispersal of smoke.

l A better understanding is needed of the interaction of these processes on smoke

production. Smoke management will be one of the most important fire management

issues in the 21st century. How should smoke effects be integrated and evaluated

in a simulation approach? How should the effects of smoke on humans and ecosystems be communicated to the public?

The final listings of key questions, their responses, and rankings for each are given

in table 2. The Workgroup did not feel that it could make priority comparisons among

the factors that impact fire disturbances because of the interrelated nature of those

associations. Practicality of the key questions also was difficult to determine owing

to tremendous uncertainties. In addition to the tabular information in table 2, the

Workgroup offered several general assessments of these links. First, besides the

importance of the fire-disturbance factors listed, it is their interactions that are truly

significant. This realization also is reflected in the importance rankings given to key

questions 1 and 2. Second, extreme fire events are driven by climate, and through

better understanding and predictability of the precipitating conditions, researchers can

greatly assist managers. Third, the probability of large-scale disturbances, combined

with cost, needs to be computed more reliably. For future needs, the Workgroup noted

that as fire suppression activities are reduced, in part due to cost and in part due to an

ecosytem-management view of fire as an important natural disturbance, smoke

management will become a central fire-management issue.

31

FIRE As A LARGE-SCALE DISTURBANCES

Background Simulating broad-scale disturbance is the terra incognita of fire modeling (Simard

1991). The process-based fire-behavior models cited in the previous chapter can be

used to simulate the real-time behavior of an individual fire at the scale of the forest

stand (Andrews 1986) or to rate daily fire danger at the scale of the National Forest

district (Deeming and others 1977). Even at these relatively fine spatiotemporal

scales, modeling of fire behavior requires making several assumptions that allow

results of experiments in fire-research laboratories to be extrapolated to more heterogeneous conditions in the field. Perhaps the most critical, and frequently unsupported,

of these assumptions is that fuel properties are homogeneous in both space and time

(Rothermel 1972). It is a testament to the robustness of fire-behavior models that,

even under this weak assumption of fuel homogeneity, their performance is generally

adequate at the scales at which they are currently applied (Simard 1991).

Fire-Disturbance

Impacts

Processes at temporal scales longer than a day and spatial scales larger than a

National Forest district are poorly understood, and empirical data generally are not

available at these scales (McKenzie and others 1996a). Nevertheless, there is an

increasingly critical need to relate wildland fire to broader scale issues, such as the

potential impact of global climatic change on terrestrial ecosystems (Gardner and

others 1996, Ryan 1991). The composition and function of ecosystems are constrained

1 Workgroup members who developed this section: Carlos Avalos,

Sarah Brace (Recorder), Joseph Fall, James Lenihan (Leader),

David Peterson, and David Sandberg.

33
Fuel and Weather

Heterogeneity

3 4

by disturbance, and ecosystem change often occurs as abrupt transitions owing to

changes in disturbance regimes (Davis and Botkin 1985). Global climatic change is

predicted to significantly alter disturbance patterns (Overpeck and others 1990) and

thus ecosystem change could be sudden and extensive. Fire regimes may be especially sensitive to climatic change (Clark 1990a,1990b), and changes in the frequency

and severity of fire could be more important near-term determinants of rates of ecosystem change than are the more direct effects of global warming. A pulsed transfer of

carbon to the atmosphere accompanying more severe fire regimes could contribute

further to global warming and ecosystem instability (Neilson and King 1992, Neilson

and others 1994).

Broad-scale simulation of the impact of fire will require a new approach to fire modeling that incorporates components and concepts not part of existing systems; for

example, the focus at broader scales likely will shift from fire behavior and fire danger

to the system-specific impacts of fire encompassed by the poorly defined concept of

fire severity (Simard 1991). Unlike physical-based measures of fire behavior (e.g., rate

of spread, fireline intensity) and the various indices of fire danger, broad-scale measures

of fire severity would necessarily be system specific. Fire severity from the standpoint

of the impact on ecosystems might be measured by the percentage of vegetation killed

or the ioss of soil nutrients, while the emissions of different gaseous and particulate

species would be appropriate measures of the impact on the atmosphere. Fire

occurrence would be better expressed in terms of the fire cycle or annual percentage

of area burned, in contrast to the fire frequency and return-interval statistics more

appropriately applied at the scale of the tree or forest stand (Johnson and Gutsell

1994). The broad-scale relation between fire occurrence and fire severity (i.e., the fire

regime) could be represented by system-specific frequency-intensity curves (Pyne

1984). As in the analysis of flood history, these curves could be used to characterize

the relative severity of lo-, 20-, 50-, or loo-year events, replacing the more generalized descriptions of fire regimes (Agee 1993) that have limited utility for long-term

planning.

The relative heterogeneity of fuels and weather in space and time is a fundamental

determinant of fire severity, so simplifying assumptions of homogeneity characteristic

of fire modeling systems at finer scales would seem inappropriate in a broad-scale fireseverity model. Greater spatial heterogeneity of fuel properties, weather, and topography

generally promotes lower fire severity at landscape to regional levels. Fire severity at

the stand level may be high at select positions in the landscape, but at the broader

scale and under normal weather conditions, spatial heterogeneity tends to produce a

low-severity regime characterized by a patchy distribution of relatively small fires

(Heinselman 1985, Minnich 1983). Forces that alter spatial heterogeneity tend to alter

the intensity and extent of fire. For example, timber-harvesting systems that increase

the fragmentation of the landscape can reduce connectivity from the standpoint of fire

spread (Green 1989, Turner and others 1989), thus decreasing average fire size. On

the other hand, fire-suppression policies tend to increase both the homogeneity and

flammability of landscapes and can lead to more extensive and higher intensity fire

(Habeck 1985). Insects and wind can increase or reduce landscape fragmentation,
Implications for

Modeling

depending on the scale, pattern, and intensity of the disturbance, with consequent

effects on the broad-scale fire regime (Knight 1987). Fire by itself, or in concert with

other agents of disturbance, can alter the level of spatial heterogeneity and thus

influence the severity of subsequent events (Lotan and others 1985).

To estimate broad-scale fire severity, it may not be necessary to model the impact of

fire across all fire intensities and extents that occur on a landscape. The vast majority

of fires, although important in the maintenance of ecosystem structure and function

and the spatial heterogeneity of landscapes, may nevertheless be insignificant from

the standpoint of broad-scale fire severity. Only a very low percentage of fires are, in

fact, responsible for a very high percentage of the fire-caused damage to ecosystems,

the atmosphere, and society (Strauss and others 1989).

Infrequent, high-intensity fires of large extent are commonly associated with a specific,

synoptic-scale sequence of weather events. A combination of high temperatures and

high winds reduces the spatial heterogeneity in fuel flammability and further increases

the burn connectivity of the landscape through wind-driven enhancement of fire

spread. Typically, a blocking high-pressure system with a duration of a month or more

promotes extreme and extensive drying of fuels through prolonged high temperatures,

low humidity, and light winds. Partial or complete breakdown of the high-pressure

ridge followed by a cold front passage or the buildup of convectional storms provides

the lightning and wind that ignite and promote the spread of one or more fires through

drought-conditioned, highly flammable fuels (Johnson 1992). Essentially the same

relation between the incidence of high-severity fire and this specific synoptic-scale

weather sequence has been reported for systems as disparate as the boreal forests of

Canada (Bessie and Johnson 1995, Payette et al. 1989, van Wagner 1978) maritime

coniferous forests of the Pacific Northwest (Huff and Agee 1980, Pickford and others

1980) and ponderosa pine (finus ponderosa Dougl. ex Laws.) forests of the Southwestern United States (Swetnam and Betancourt 1990).

In a broad-scale fire-severity model, the relatively infrequent occurrence of large, highintensity fires could be predicted as a function of the duration of drought produced

(Renkin and Despain 1992) by a blocking high-pressure system. The drought code in

the Canadian forest fire weather index system (van Wagner 1987), the Keetch-Byram

drought index (Keetch and Byram 1988), or the estimated percentage of moisture of

the 1 OOO-hour fuel time lag class (Ottmar and Sandberg 1985) could each serve as an

index of extended drought. A threshold of the drought index together with some index

of lightning activity (e.g., Price and Rind 1992, 1994) would signal the occurrence of a

severe fire in the model. Behavior (e.g., surface and crown fire spread, fireline intensity, smoldering combustion) and impacts (e.g., extent and degree of vegetation

damage, nutrient loss, gaseous and particulate emissions) would be modeled by using

existing fire spread and first-order fire-effects models (Keane and others 1994).

In broad-scale application of relatively fine-scale models, an adequate representation

of the variation in model inputs due to landscape-level spatial heterogeneity would be

necessary to assure realistic results. One approach might be to divide the landscape

up into land-surface types (Avissar and Pielke 1989, Keane and others 1995), perhaps

3 5
by physiographic position, and to run the array of fire-behavior and fire-effects models

for each distinct type, assuming within-type homogeneity of model inputs. The broadscale severity of the event for the entire landscape could be estimated by an areaweighted average of the results for each distinct land-surface type.

Key Questions

and Responses

It may not be necessary to model the behavior and effects of frequent, low-severity fire

to the extent done for severe fire in a broad-scale fire-severity model; for example,

impacts on ecosystems or the atmosphere produced by low-severity fires (i.e., the

majority of events) could be represented implicitly by model parameterizations that

produce constant (or episodic) but relatively low levels of mortality, nutrient loss, or

emissions in broad-scale simulations. These parameterizations could be specific even

for different land-surface types to represent variation in frequency and intensity of

relatively small-scale events across a heterogeneous landscape.

The key questions proposed for this Workgroup (fig. 4) dealt with fire at large scales, in

particular (1) spatial and temporal dynamics, (2) the ecological role of fire, (3) management of fire, and (4) the critical components of the fire-behavior environment. Owing to

the broad scope of these key questions, the Workgroup felt that more specific and

directed questions would better enable meaningful discussions. The Workgroup

therefore identified 17 focused questions for the four key questions. Importance

rankings were developed first for the four key questions and subsequently among the

focused questions within each key question. Key questions and focused questions

appear below in descending order of importance. Responses were developed for the

two or three most important focused questions under each key question. Each response was identified by its characteristic scope (i.e., global to local). The responses

identified for a focused question were then ranked in importance. No rankings were

developed for practicality aspects of any questions or responses. A summary of the

importance ratings for key questions and responses appears in table 3.

Table 3-Fire as a large-scale disturbance key questions and focused questions

are rated according to importance

Key questions and focused questions” lmportanceb

1. What are the critical aspects of spatial and temporal dynamics

of fire at large scales? 0.41

What characteristics of fire as a landscape-ecosystem disturbance

are relevant to large-scale (spatial and temporal) modeling?

What are the characteristics or forces that drive the behavior

of a fire regime? 0.29

What is the feedback of fires on the greenhouse effect? What is

the long-term interaction of fire, ecosystem structure, and climate?

What role will potential climatic change have on fire regimes?

How will fire frequency control vegetation composition with

climatic change? 0.25

What is the relative importance of the cumulative impact of small fires

versus the impact of rare large fires or extreme events? 0.15

3 6
Table 3-Fire as a large-scale disturbance key questions and focused questions

are rated according to importance (continued)

Key questions and focused questionsa lmportanceb

How do we deal with heteroaeneitv in modeling large-scale disturbance?

How important as refuqia for fire-sensitive species are areas missed by

fires over several events? What is the nature of areas that are refugia?

What characteristics of these areas allowed them to be missed by

fire events?

How do we deal with the stochastic nature of single events in fire regime?

2. What ecological role does fire play at larger scales?

What are the most important aspects of long-term changes in fire

characteristics on vegetation? How is fire interrelated to other

disturbance vectors? Does fire create stress in ecosystems or result

from stress in ecosystems?

How does fire (regime and individual) impact ecosystem processes

and dynamics?

What influence does past disturbance history have in shaping the

current ecosystem structure (e.g., looking at two drainages that share

the same disturbance regime)?

3. How can fire be manaaed at large scales?

How does landscape fraamentation affect large scale fire regimes?

What characteristic of a fire regime has the most importance

(carries value) to the public?

How is appropriate fire regime defined for manaaement obiectives?

What are the relevant landscape and large-scale issues for political

boundaries (management and policy differences)?

In a non-steady-state environment, how does one chose to manage

for a particular landscape?

4. What are the critical characteristics of the fire-behavior environment?

Under what circumstances does crowning potential become the

critical aspect of fire behavior for predicting effects?

In which environments can it be assumed that ignition sources are

always available?

How important is fire size as a feature of the fire regime?

0.15

0.09

0.08

0.28

0.38

0.38

0.24

0.17

0.27

0.23

0.20

0.16

0.13

0.15

0.51

0.30

0.19

“Underlined words are used as a shorthand notation in analyses in tables 15-20, p. 72-74.

bThe Workgroup did not compare questions with respect to practicality.

3 7
Question 1: What are the critical aspects of spatial and temporal dynamics of

fire at large scales?

A. What characteristics of fire as a landscape-ecosystem disturbance are relevant to

large-scale (spatial and temporal) modeling? What are the characteristics of forces

driving the behavior of a fire regime?

l Fuel conditions are relevant at small and large spatial scales (global).

l Fluctuation in climate, even at small temporal scales, will be important for

modeling fire at large scales (regional).

l Temporal variation and dynamics in fuel conditions affect large-scale fire regimes (regional).

l Health of ecosystems is the most important determinant of disturbance at large

scales (regional).

l Vegetation structure, abundance, and distribution affect large-scale fire patterns

(biome).

l The range of variability in fire characteristics is more important than mean fire

characteristics when modeling at larger scales (global).

l Fire frequency affects large-scale fire patterns (global).

B. What is the effect of fires on the greenhouse effect? What is the long-term interaction of fire, ecosystem structure, and climate? What role will potential long-term

temperature increases due to climatic change have on fire regimes? How will fire

frequency control vegetation composition through climatic change?

l If climatic change results in long-term increased temperatures, this will result in

an increase in fires, because it will affect the availability of fuels (regional,

especially at northern latitudes).

l As ecosystems come under stress, a pulse of carbon will be released into the

atmosphere from increased numbers and severity of fires (global).

l The relative impact of changes in fuels and climate will differ with ecosystem

(global).

l Changes in fire frequency and intensity will change vegetation composition and

structure (regional).

l An increase in fire frequency will have a negligible to slightly negative effect on

greenhouse gases buildup; there will be a greater effect on ecosystem health

and recovery than on the release of stored carbon (increased decomposition will

have a greater impact than will accelerated carbon release) (forest biomes).

l Changes in fire regime will have a greater impact on northern-latitude ecosystems relative to carbon and nutrient cycling than on southern-latitude ecosystems (regional).

l Increased fire frequency will enable favorable conditions for life forms that can

take advantage of new climatic conditions (global).

3 8

r- /
C. How do we deal with the stochastic nature of single events in fire regimes?

D. How important are areas missed by fires over several events as refugia for firesensitive species? What is the nature of areas that are refugia? What characteristics of these areas allowed them to be missed by fire events?

E. What is the relative importance of the cumulative impact of small fires versus the

impact of rare large fires or extreme events?

F . How do we deal with heterogeneity in modeling large-scale disturbance?

Question 2: What ecological role does fire play at larger scales?

A. What are the most important aspects of long-term changes in fire characteristics

on vegetation? How is fire interrelated with other disturbance vectors? Does fire

create stress in ecosystems or result from stress in ecosystems?

l Spatial patterns and distributions of species change under different fire regimes

(global).

l Changes in fire frequency, size, and intensity will change postfire vegetation

composition (regional).

l Fire interactions with wind and insect disturbance can be as important as fire

acting as the sole disturbance (regional).

l Fire can play a role in revitalizing an ecosystem (can relieve stress); fire is more

likely to occur in a stressed ecosystem (regional).

l One disturbance can mitigate or propagate another disturbance, depending on

heterogeneity in the system and the relative scale of the processes (global).

B. How does fire (as a regime and as individual events) affect ecosystem processes

and dynamics?

l Fire can affect the nutrient status and productivity of a given site (global).

l Fire can influence site water availability (global).

l Fire occurrence in ecosystems from which fire has been excluded can alter

landscape patterns and disrupt previous ecosystem structure and functional

relations (regional).

l Fire accelerates biogeochemical processes (e.g., carbon flux) (global).

9 Fire mobilizes stored carbon (distinct from other elements that can be cycled

back into the system) (global).

l Fire in a stressed ecosystem will accelerate succession (forest biomes).

. Fire is important in maintaining a range of successional states across the

landscape (global).

l Fire may increase the rate of species response to new climatic conditions

(ecosystems with long-lived species).

3 9

P-
C. What influence does past disturbance history have in shaping current ecosystem

structure (for example, looking at two drainages that share the same disturbance

regime)?

Question 3: How can fire be managed at large scales?

A. How does landscape fragmentation affect large-scale fire regit’neS?

l Silvicultural practices decrease the average fire size by imposing a finer scale of

disturbance (regional).

l Fire management can increase or decrease heterogeneity in the landscape

(global).

l Manipulation of fuel loading can mitigate the impacts of landscape fragmentation

on fire regime; modification of fuel loading can influence fire frequency (regional).

l Much larger fires will result from a reduction in heterogeneity; the landscape will

become increasingly homogeneous, resulting in an increase in fire size (regional).

B. What characteristics of a fire regime have the most importance (provide value) to

the public?

l Fire effects on aesthetics, property, health, and safety (human) are the most

important values to the public (regional).

l Smoke production is perceived as a negative impact on visibility (regional).

l Perceptions of fire are different depending on social, cultural, and economic

factors, as well as proximity to potential burns (regional).

9 Large fires are acceptable to the public under certain situations (e.g., in parks

and wilderness areas) (regional).

l The public potentially may support the concept that fire can increase safety

(subregional).

C. How should fire regimes be defined for resource management objectives?

l Appropriate fire regimes are defined by management objectives, not simply by

ecosystem characteristics (global).

l Management objectives need to be stated explicitly (global).

l The historic fire regime should be considered in the development of resource

management policy; we need to understand how systems have developed

without placing value judgment (regional).

l Managed fire regimes should not cause degradation of ecosystem components

(e.g., erosion, accelerated nutrient cycling, species change) (global).

l Resource managers must understand ecological responses to different fire

regimes before setting objectives (global).

40
D. What are the relevant landscape and large-scale issues for political boundaries

(management and policy differences)?

E. In a non-steady-state environment, how do you chose to manage for a particular

landscape?

Question 4: What are the critical characteristics of the fire-behavior

environment?

A. Under what circumstances does crowning potential become the critical aspect of

fire behavior for predicting effects?

l Surface and crown fires are different disturbances with different ecological

effects (forest biomes).

l The best predictors of crown fires are different at different scales (forest biomes).

l The mechanisms under which crown fire is propagated (threshold conditions)

are poorly understood and difficult to model (forest biomes).

B. In which environments can we assume that ignition sources are always available

versus scarce?

l Ignition sources on east-side (dry) versus west-side (wet) ecosystems (e.g., in

the Cascade Range and Rocky Mountains) are different (regional).

l Ignition sources in ecosystems with frontal versus continental climates are

different (continental).

l Flammable conditions are a necessary requirement, but the relative importance

of ignition sources differs with environments (including climatic conditions)

(global).

. Ignition sources are always available at urban-wildland interfaces with high

human populations (local).

l Selected human activity can increase ignition frequency, even if human populations are low (local).

l Process-based simulation of lightning is complex but relatively robust; statistically based approaches may be less complex but less robust (global).

C. How important is fire size as a feature of the fire regime?

41
Synopsis

4 2

Landscape-level changes resulting from fire are difficult to model owing to climatic and

vegetation heterogeneity, lack of empirical data at large scales, and limited spatiotemporal scope of existing models. Because ecosystem composition and function change

where disturbance regimes change, there nevertheless is a critical need to model

large-scale disturbances. Because of time constraints, no practicality comparisons

were made for any of the questions, and responses were generated only for the most

important focused questions under each key question.

The Workgroup felt that the broad key questions proposed in the straw man document

(fig. 4) needed to be further refined. Focused questions were used to provide that

refinement so that responses could be easily proposed. Focused questions for critical

aspects of spatiotemporal dynamics included landscape-level disturbance characteristics of fire, the stochastic nature of single fire events, ecological importance of fire

refugia, and the relative importance of small-fire cumulative impacts versus extreme

fire events. The large-scale ecological role of fire can be refined as vegetation impacts, interactions with other disturbances, impacts on ecosystem processes and

dynamics, and ecosystem structure resulting from past disturbance history. To answer

large-scale management questions, scientists and managers need to address landscape fragmentation effects on fire regimes, fire regime characteristics that the public

values, definitions of fire regimes for management objectives, large-scale issues for

political boundaries, and management for a particular landscape in a non-steady-state

environment. The critical characteristics of the fire-behavior environment include

importance of crowning to fire-behavior predictions, ignition source abundance, and

fire-size importance to describing a fire regime. At ecosystem and landscape levels,

there is much that needs to be better understood about fire as a large-scale disturbance if fire is to become part of future management strategies.
Background

FIRE-EFFECTS M ODELING S TRUCTURES’

The effects of fire on ecosystems has been a primary concern for resource managers

in the United States for over 100 years. A great deal of research has been conducted

since the early 1900s to describe and understand fire-effects, but the majority of the

research has been conducted at the individual tree or stand scale, even though the

results have been applied at larger spatial scales (McKenzie and others 1996a,1996b).

For example, early fire-suppression policies that affected millions of hectares in the

Western United States were based on fire-effects research at the stand scale (Fritz

1932; Schiff 1962; Show and Kotok 1923,1924).

Resource management for the 1990s and beyond will require an understanding of

ecological processes at spatial scales larger than the stand scale. Accurate simulation

models will be needed to predict the outcomes of complex interactions among disturbances (particularly fire), climatic changes, and large-scale vegetation patterns. A

principal difficulty in building large-scale fire-effects models is the extrapolation, or

aggregation problem (Cale 1995, King and others 1991, McKenzie and others 1996a,

Rastetter and others 1992). In the past decade, models have been developed to

’ Workgroup members who developed this section: Ernest0

Alvarado, Mark Finney, Donald McKenzie, Carol Miller, Ronald

Neilson, Lisa Snydal (Recorder), and David Weise (Leader).

4 3
Key Questions

and Responses

4 4

predict fire ignitions, fire behavior, fire effects, and vegetation change in response to

fire (see previous sections). Many of these models partially address the aggregation

problem, but each type of model has identifiable sources of error when applied at

broad spatial scales.

Scale issues and the aggregation problem framed the discussion and recommendations of this Workgroup. Several key questions directly addressed scaling and aggregation error, but other more technical questions were motivated by previous difficulties

in addressing these issues within models. In the following discussion, the term “fire

effects” refers not only to first-order fire effects (e.g., crown scorch, cambial kill, tree

mortality) but also to broader scale effects (e. g., altered successional pathways,

vegetation mosaics, landscape dynamics).

The Workgroup formulated 10 key questions, expanding on 3 of the questions in the

straw man document (fig. 4) and identifying 7 others more directly related to modeling

structures. Key questions were considered for importance, but not practicality.

Workgroup members felt that key questions were too broad to enable them to make

meaningful practicality comparisons. Key questions appear below in descending order

of priority value. As time permitted, responses to the most important key questions

were ranked by both importance and practicality. Bulleted lists following each key

question enumerate responses. Key questions and their responses are listed along

with rating scores in table 4. Workgroup members agreed that all key questions are

relevant to both broad (regional, national, continental, and global) and narrow (plant,

stand, watershed, ecoregion) scopes. Temporal scales associated with broad versus

narrow scope were not delineated; moreover, the Workgroup found that questions

regarding temporal issues were less precisely formulated, and less easily answered,

than those regarding spatial issues.

Question 1: How does one validate a model’s structure with respect to error

propagation?

Models cannot be proven, only disproven, but confidence levels can be estimated for

model outputs. Validation implies that data are available for comparison. Large

amounts of data with both spatial and temporal depth are needed. Model structure

affects how error propagates through a model; therefore, validating structure of a

model is part of the process.

l Analyze of the sensitivity of the internal components of a model to both data and

interactions with other models. We also need sensitivity analysis of transitions

among model components where there is spatial or temporal aggregation.

l Compare outputs of a model and model components to independent data.

l State the operational bounds for model inputs.

l Compare similar models to each other and with independent data.

l Compare model structures to structures from previously validated models. Disparate spatial and temporal scales of model application may, however, require different

model structures.
Table 4-Key questions and their responses for fire-effects modeling structures

are rated according to importance and practicality

Key questions and response9 Importance Practicalityb

1. How does one validate a model’s structure with

respect to error propagation?

We need analyses of the sm of internal components

of the model to both data and to interactions with other

models; we also need sensitivity analysis of transitions

between model components at which there is spatial or

temporal aggregation

Cm outputs of a model and model components to

independent &&

State the operational bounds for model inputs

Compare similar models to each other and with

independent data

Compare model structures to structures from previously

validated models; disparate spatial and temporal scales

of model application may, however, require different

model structures

2. What are the relevant spatial and temporal scale issues

(including extent and resolution) related to modeling

fire effects?

Modeling needs to be spatially explicit and temporally

dynamic; model resolution needs to be finer than the

extent desired for projection

Spatial and temporal variability in weather and climate,

vegetation, fuels, and fire behavior is different at

different scales

The appropriate temporal and spatial resolution and extent

need to be determined for each modeling effort

Considerations of temporal aaareaations are as important

as consideration of spatial aggregations

The structure of fire-effects models may be different at

different spatial and temporal scales

Most existing data on fire effects have been collected and

analyzed at small spatial and temporal scales

The magnitude of the error needs to be quantified relative

to the scale of implementation

0.22

0.27 0.12

0.26 0.17

0.25 0.34

0.14 0.17

0.08 0.21

0.18

0.22

0.16

0.15

0.13

0.10

0.10

0.08

0.11

0.17

0.18

0.11

0.14

0.12

0.10

4 5
Table 4-Key questions and their responses for fire-effects modeling structures

are rated according to importance and practicality (continued)

Key questions and responsesa Importance Practicalityb

Models need to be designed to minimize errors at the

intended scale of implementation

3. What are the “ideal” fire-effects model outouts?

Produce spatially explicit and immediate fire-effects

outputs and generate necessary inputs for successional

vegetation models

Include physical and biological aspects so that the model

has broad applicability; that is, it is process based

Relate fire behavior (flaming and smoldering combustion)

to fire effects; flaming combustion is typically associated

with the fire front, and smoldering combustion occurs after

the fire front passes or in ground (peat) fires

Produce quantitative emission characteristics and timedependent emissions

4. How does one calibrate a fire-effects model?

Individual components of the model should be calibrated

separately

To the extent possible, the model should be calibrated

across the domain of the anticipated implementation

Calibrate against theoretical standards, so that calibration

is more than a sequence of adjustments to make the

output “look correct”

Resolution of the model should be consistent with the

resolution of the data used for calibration

Calibrate against a large amount of data

Calibrate against another model

5. How does scale affect the modeling approach?

As the resolution of the model changes, the approach to

modeling changes (e.g., from process based to statistical);

statistical properties of aggregates often are more easily

estimated and modeled than components of these

aggregates

In the real world, the temporal and spatial scales of processes are variable; thus aggregation error will occur

when time steps and spatial resolution of different modeled

processes are equalized

0.06

0.14

0.07

0.33 0.30

0.30 0.22

0.28 0.25

0.10

0.12

0.24

0.24 0.23

0.22 0.12

0.16 0.18

0.16

0.13

0.09

0.09

0.75

0.25

0.15

0.10

0.21

0.50

0.50

4 6
Table 4-Key questions and their responses for fire-effects modeling structures

are rated according to importance and practicality (continued)

Key questions and responses* Importance Practicality*

6. What are the “ideal” fire-effects model components? 0.06

7. What &&a are available for calibration, validation, and

development of fire-effects models? 0.06

6. What is the appropriate system structure (e.g., an

integrated system of separate models or a unified model)? 0.05

System modularity should reflect process modularity 0.31

If model structure involves coupling independently

developed models, internal consistency between

analogous modules should be ensured and redundancy

should be reduced 0.29

Where possible, process-based models are preferred over

statistical models. 0.26

The model should be structured to be as modular as possible 0.14

9. How does one integrate climate into fire-effects modeling? 0.04

10. What tools exist to generate data for the development

of fire-effects models? 0.04

0.27

0.23

0.16

0.31

‘Underlined words are used as shorthand notation in analyses in tables 21-27, p. 76-79.

bThe Workgroup did not compare questions with respect to practicality.

Question 2: What are the relevant spatial and temporal scale issues (including

extent and resolution) related to modeling fire effects?

Fire effects occur across a wide range of spatial and temporal scales; for example,

individual trees may be affected by fire while nutrient losses occur at the watershed

scale, and the consequences of immediate fire effects (tree mortality, etc.) are felt over

decades or centuries. Thus, translating information across scales is essential in any

modeling effort. Although it may not be explicitly stated, scale is implicit in all questions posed by land managers. The scale of interest dictates the modeling approach,

where the model is applied, and the types of data used in model development, calibration, and validation.

l Modeling needs to be spatially explicit and temporally dynamic. Model resolution

needs to be finer than the extent desired for projection.

l Spatial and temporal variability in weather and climate, vegetation, fuels, and fire

behavior is different at different scales.

l The appropriate temporal and spatial resolution and extent need to be determined

for each modeling effort.

4 7
l Considerations of temporal aggregations are as important as consideration of

spatial aggregations.

l The structure of fire-effects models may be different at different spatial and temporal

scales.

l Most existing data on fire effects have been collected and analyzed at small spatial

and temporal scales.

l The magnitude of the error needs to be quantified relative to the scale of

implementation.

l Models need to be designed to minimize errors at the intended scale of implementation.

Question 3: What are the desired outputs of an ideal fire-effects model?

Here we refer to first-order fire effects. The output of a fire-effects model provides

information needed by other models and by researchers and policymakers. Before a

useful fire-effects model can be developed, we need to know what types of information

are desired by policymakers and land managers. The desired information will dictate

the appropriate scale of the model and the approach that should be taken. The model

should:

l Produce spatially explicit and immediate fire-effects outputs and generate necessary inputs for successional vegetation models.

l Include physical and biological aspects so that the model has broad applicability;

i.e., be process based.

l Relate fire behavior (flaming and smoldering combustion) to fire effects. Flaming

combustion is typically associated with the fire front, and smoldering combustion

occurs after the fire front passes or in ground (peat) fires.

l Produce quantitative emission characteristics and time-dependent emissions.

Question 4: How does one calibrate a fire-effects model?

Calibration is crucial for accurate parameters in models so that the models produce

outputs consistent with observations from the real world. Calibration works in tandem

with validation so that we can have confidence that a model will perform well under

conditions outside the range of current experience. Often we do not have all the data

needed to begin a model or sufficient data density to know the model is accurately

representing the real world.

l Calibrate individual components of the model separately.

l To the extent possible, calibrate the model across the domain of the anticipated

implementation.

. Calibrate against theoretical standards, so that calibration is more than a sequence

of adjustments to make the output “look correct.”

48
. Maintain consistency of model resolution with the data resolution used for calibration.

l Calibrate against a large amount of data.

l Calibrate against another model.

Question 5: How does scale affect the modeling approach?

The scale of the application affects the structure of the model and affects the nature of

the information obtainable from the model.

l As the resolution of the model changes, the approach to modeling changes (e.g.,

from process based to statistical). Statistical properties of aggregates often are

more easily estimated and modeled than components of these aggregates (Levin

1992).

l In the real world, the temporal and spatial scales of processes are variable. Thus

aggregation error will occur when time steps and spatial resolution of different

modeled processes are equalized.

Question 6: What are the components of an ideal fire-effects model?

The reason for describing an ideal model for fire effects is to provide the context for

assessing existing models. Similarly, components of an ideal fire-effects model need

to be identified for comparison with components of existing fire-effects models. This

will put current knowledge in perspective and identify shortcomings of current models

and their components. It also will help ongoing efforts to improve our models, so that

they will be useful as components of ecological modeling at multiple scales. Outputs

of an ideal fire-effects model, discussed above (see question 3) will determine, to a

great extent, the components of such a model. Responses to that question also are

appropriate here.

Question 7: What data exist for calibration, validation, and development of fireeffects models?

For years, fire research has been fragmented in time and space. Until recently, little

effort has been made to maintain long-term fire-effects research. With the availability

of new computing and satellite technology and associated databases, modeling fire

effects at large spatial scales is more feasible. Fire-effects modelers need a rigorous

methodology to compile and integrate available databases.

Most fire effects databases cover short periods and small spatial scales. Current

large-scale assessment efforts (e.g., SNEP 1996 and the Columbia River basin

assessment [Quigley and others 19961) present an opportunity to validate and develop

new fire-effects models for larger spatial scales. The main shortcoming to date is the

lack of long-term data. The sites that have been maintained (e.g., Long-Term Ecological Research [LTER] sites and research forests) cover relatively small areas. Much of

the available data and model documentation is in the files of Federal and state land

management agencies.

4 9
50
Current tools for spatial interpolation range from geostatistical methods (e.g., kriging)

to regression-based methods that explicitly incorporate topography (e.g., PRISM; Daly

and others 1994). Perhaps the most common approach to temporal interpolation is to

use statistical weather generators that maintain specific temporal autocorrelation

statistics at daily, monthly, interannual, and interdecadal timescales. Combining

temporal and spatial interpolation to simultaneously maintain temporal and spatial

autocorrelation is an emerging technology (VEMAP members 1995).

Spatially explicit time series of potential future climates also must be developed to

estimate fire effects in changing climates. Perhaps the most common approach for

this is to use output from general circulation models (GCMs). The GCMs produce

physically consistent weather output at timesteps of about 20 to 40 minutes over very

coarse grids, for example, 4 to 5 degrees of latitude-longitude resolution. Because the

grids are coarse, the global climate is simulated over a crude topography and does not

adequately reflect the observed climate, particularly in mountainous regions. Future

climatic scenarios therefore are developed from GCMs by calculating deltas (ratios or

differences) between simulated current and future climate. The deltas for each climate

variable are then interpolated back to the baseline observed climate at the resolution

of the baseline climate (VEMAP members 1995). Such interpolation is done to carefully select and preserve temporal autocorrelation statistics produced by the GCM or

existing in the baseline data.

Question 10: What tools exist to generate data for the development of fireeffects models?

Synopsis

Data gaps occur for many geographic areas for which we need fire-effects predictions.

It is expensive and time consuming to gather relevant field data, particularly fire

histories. Additionally, empirical data often are not in a form useful for modeling; a high

cost currently is associated with adaptation of field data. Most research programs

have developed tools and software independently to transform field data into a format

useful for modeling; thus, existing tools are in different forms and at different locations.

Recent research has produced new technologies for data analysis and integration, and

quantum leaps in understanding fire as an integral process in ecosystems. We need

to verify what models exist, and make their documentation available, so that we do not

conduct redundant research. Most fire-effects models and data are available in

university libraries as theses and dissertations or in the files of Federal and state land

management agencies. Also, due to current societal concerns, other agencies (e.g.,

National Aeronautics and Space Administration, Department of Energy) are incorporating fire research into their programs. Thus, improved communication among researchers in different agencies is a high priority. Electronic access to compilations of data

(e.g., Fischer and others 1996) is an important first step.

We need to use the modeling process carefully to identify gaps in data, knowledge,

and theory. Fire-effects models must be allowed, for example, to be wildly wrong. If

basic model parameters are wrong or incomplete, or if the model involves significant

extrapolation across geographic areas or temporal or spatial scales, then premature

51
calibration will mask difficulties rather than improve accuracy. By quantifying the

calibration necessary to match observed data, we can estimate the importance of

missing spatial information or the magnitude of error associated with aggregation. Any

model thus can be used to identify knowledge gaps during the process of calibration.

We need to address the scaling problem systematically. Next to model validation,

scale issues are the most important questions for fire-effects modeling structures.

Although currently no simple solutions exist to the extrapolation and aggregation

problem, quantifying and minimizing errors related to scale do not seem to be important issues relative to other scale issues. Both were ranked low by the Workgroup.

Spatiotemporal variability, resolution, and extent were listed as the most important

scale issues and the most practical to address. This gives them high priority for future

modeling efforts.

From a strictly model-structure perspective, integrating climate into fire-effects modeling has a very low priority. Until the tools and protocols necessary for model validation, spatial and temporal scales, desired model outputs, and model calibration are

provided, incorporating more realistic features into models (e.g., climatic factors) will

have little impact on developing effective models. Technical knowledge of how to build

the best models must precede the building of realistic models that include climate and

other important factors.

We need to be conscious of intrinsic limits of the accuracy and precision of our knowledge and, therefore, the predictive ability of our models. If events at a particular

spatial or temporal scale are clearly stochastic, or governed by chaotic dynamic

systems, predictive ability at those scales will be low. Judicious use of state-of-the-art

aggregation techniques will be a key factor in optimizing models.

5 2

,111 n,,,u &IIci ““LYULY “I “““SA,~,” IIIVUVIY .“,““I “.“.A.“‘~ s..-

. ..---. . . .-_ . . ._-.. .-..,
MANAGERIAL CONCERNS, APPLICATIONS,

AND DECISION SUPPORT’

Background

Key Questions

and Responses

Good management rests on a foundation of solid science. Two challenges must be

met to properly integrate management and science: First, research and management

must collaborate through partnerships, and the key to this relation-building challenge is

communication; second, biological, physical, and social science knowledge must be

integrated as fire-disturbance models are developed. Fire-disturbance models are

the nexus of fire management and research and need to integrate all the sciences

to adequately provide a foundation for successful management of fire on the

landscape.

After some initial discussion covering a broad range of topics, the Workgroup settled

on a short list of key questions. These five management and application questions are

listed below, in descending order of importance (table 5). For each of the key questions, lists of responses are enumerated also in descending order of importance. The

Workgroup briefly discussed narrow- and broad-scope topics within each key question.

These are summarized within the introductory paragraphs of the following sections.

I Workgroup members who developed this section: Michael

Hilbruner, Roger Ottmar, Lucy Salazar, James Saveland (Leader),

Gordon Schmidt, Daniel Schmoldt, Robert Vihnanek, and Clinton

Wright (Recorder).

5 3
Table 5-Management concerns, applications, and decision-support key questions and their responses are rated according to importance and practicality

Kev auestions and resoonsesB lmoortance Practicalitv

1. What are the most useful model structures and outputs, to

support issues in planning, operations, monitoring, and

learning by resource managers, decisionmakers, policymakers and researchers?

Design models to allow users to select b regimes and

show their probabilistic effects on the landscape

Data structures must be compatible with user capabilities

Develop a hierarchical and selective modeling framework

for fire regimes and fire effects (e.g., LOKI)

Communicate model limitations to users, and user needs

to model builders

2. How do we improve communication between users and

model builders (scientists) relative to the development

life cycle?

Proactively seek opportunities to communicate

U long-term relations

3. How can we rapidly and effectively transfer research

information?

Improve documentation (user manuals, tutorials, online

help, etc.) and model support (technical support, programming, scientific documentation, software distribution,

and support via Internet, etc.), and apply product life cycles

Standardize and provide desired user interfaces

Explore alternate means for accomplishing data management (e,g., contracting) and technology transfer

Establish and suooort a development group

Apply free market principles (product development,

support and distribution)

4. How can we incorporate sociopolitical issues into models

and decision-support systems?

Incorporate sociological research when developing

decision-support systems

Modelers and managers must be aware of emerging

issues and anticipate future concerns

0.43 0.15

0.53 0.14

0.19 0.32

0.18 0.23

0.10 0.31

0.28 0.44

0.67 0.85

0.33 0.15

0.15 0.17

0.39 0.13

0.27 0.31

0.13

0.13

0.09

0.07

0.66

0.34

0.33

0.14

0.10

0.06

0.53

0.47

54
Table g--Management concerns, applications, and decision-support key questions and their responses are rated according to importance and practicality

(continued)

Key questions and responses’ Importance Practicality5. How can relevant interdisciplinary resource management

issues be incorporated into models? 0.06 0.18

Improve communication between modelers and users 0.61 0.40

Involve a cross-section of managers and policymakers in

model development 0.29 0.38

Assion responsibility, develop measurement criteria,

monitor accomplishment, and provide accountability for

both research and management 0.10 0.22

lUnderlined words are used as shorthand notation in analyses in tables 28-35, p. 82-85.

Question 1: What are the most useful model structures and outputs to support

issues in planning, operations, monitoring, and learning by resource managers,

decisionmakers, policymakers, and researchers?

This question really addresses two issues-model structures and model outputs.

First, model structures need to reflect the important effects and properties of fire

behavior to adequately model fire-related phenomena (model realism). Second,

models must provide meaningful output with diverse uses (model functionality) for

many users. This question covers the specific and critical integration issues between

model builders and model users.

The Workgroup felt that issues narrow in scope would occur at the watershed scale

and smaller, and broad issues would cover regions the size of a river basin and larger.

No specific issues were enumerated at either scale for this key question.

A. Models should allow users to select fire regimes and show their probabilistic effects

on the landscape. Although fire occurrence, behavior, and effects are deterministic

phenomena at a basic physical level, we are unable to reliably predict the resulting

complex system of low-level interactions in terms of higher level events. The larger

scale events that we observe therefore appear stochastic. Spatial and temporal

patterns of fire occurrence, for example, affect a number of important landscapescale features and determine the sizes of openings, vegetation succession, and

hydrologic events. But because fire regimes are uncertain in time and space, their

landscape effects also are uncertain. Models consequently should allow users to

select various spatial and temporal patterns and then output different stochastic

scenarios that might result from those initial conditions. Such a model would be

extremely helpful to many users, such as landscape planners, policymakers, and

researchers.

5 5
B. Data structures must be compatible with user capabilities. Models are only as

good as the data used to drive them. It makes no sense to develop a fire-spread

model that requires detailed fuels data, if those data are typically unavailable to

model users. Model developers need to be aware of which data can be readily and

reliably collected by users. Otherwise, models will be unusable or, worse, used

inappropriately with data for which the model was not designed.

C. Develop a hierarchical and selective modeling framework for fire regimes and fire

effects (e.g., LOKI; Keane and others 1996b). In the past, model development and

application have been highly fragmented. For the manager to accomplish a

specific task, a number of different models may be needed. Someone wishing to

plan a prescribed fire, for example, might use fire-behavior models, fire-effects

models, and vegetation-succession models. There may be any number of different

fire-effects models to choose from. An integrated, flexible, and modular framework

needs to be developed so that each application task-fire behavior to fire effects to

vegetation succession-flows naturally, both conceptually and operationally. As the

research and technology develop, it should be possible to add new modules and

update old modules. The technical complexity of the models needs to be hidden

from the user behind a standard, intuitive user interface (see question 3, response B).

D. Provide knowledge of model limitations to users and of user needs to model

builders. Important concepts underlying a model and model structures need to be

communicated to users. These concepts often limit what a model can do and how

it should be used, and thus should be communicated in an easily understood way

within the user documentation accompanying the model. Limitations inherent in a

model also should be incorporated into the model’s user interface so that those

limitations can be expressed to users and the interface can prevent inappropriate

uses of the model. Knowledge of these intimate details of a model by users will

help ensure that models are used correctly and results are interpreted properly.

Managers, planners, policymakers, and researchers need to communicate their

needs to model builders as well. They need to specify to modelers the types of

decisions that they make and what model output will help with those choices. As

noted above, they also need to convey what types and resolutions of data they

have available or are able to collect. Both of these communication channels can

be most effective when they are active simultaneously as modelers design and

develop models and users provide feedback on model utility.

Question 2: How do we improve communication between users and model

builders (scientists) relative to the development life cycle?

The final response to the previous key question dealt with communication between

model builders and model users to exchange model-critical information between them.

Question 2 more generally addresses the communication environment during the

model-development life cycle. This life cycle includes planning, design, development,

testing, and delivery. Bidirectional communication is needed throughout this process,

but how is an environment established that fosters such collaboration? Simply put,

creating such an environment can succeed only if there are active, ongoing efforts to

do so.

56
Several issues of a narrow focus were identified. None of them is specific to the

Pacific Northwest, and they easily could be considered just as applicable in other

contexts.

l Managers must assure the availability of researchers with needed expertise to

address regional problems and questions.

l Regional issues must be accounted for in national research efforts.

l Better procedures must be built to allow managers and scientists to participate in

the decisionmaking process.

l In a broader focus, communication with international researchers is an important

issue.

A. Proactively seek opportunities to communicate. Many potential opportunities exist

for model builders and users to communicate, such as coordinating data standards,

establishing decisionmaking needs, setting important temporal and spatial scales,

and dealing with nonpublic lands. But little will be accomplished until one group or

the other takes the first step. Both groups must realize that they need to seek out

the other on issues of mutual concern. Gatherings that are not project specific,

such as regional workshops, can be used to bring everyone together for informal,

generic discussions and to start planning future projects. Project-specific communication, on the other hand, targets detailed issues pertaining to a singular application of concern to a particular modeling group and managerial group. In either

case, both sides must feel that they can measurably gain something by actively

pursuing collaboration.

B. Build long-term relations. Regional, multigroup collaborations tend to be open

ended and, therefore, long term. They can suffer from a lack of specificity often

associated with cooperative efforts that are not project directed. Void of a focus,

interest by members in large, regional relations can wane unless specific targets

are established for group accomplishment. Project-specific applications, on the

other hand, tend to exist for a limited time because of their specialized focus. Any

significant project, no matter how specific, will often require a multiyear effort-not

exactly short term. Additionally, project-specific applications can lead to other

projects and eventually can attract other cooperators along the way. In a bandwagon sense, single-project collaborations often seed future efforts beyond the

scope of the initial project.

Question 3: How can we rapidly and effectively transfer research information?

Models allow us to transfer research results in a form that permits application to

managerial problems. This mode of transferring research results, however, is not

without complications. Research information in the form of models is encapsulated as

simplifications of reality; consequently, adjuncts, protocols, processes, and development climates are needed to support and enhance this transfer mechanism. These

things help ensure that (1) the correct information is transferred, (2) it is transferred

reliably, (3) it is applied as intended, (4) once transferred, it is relatively easy to incorporate into application, and (5) this process can occur expediently and smoothly.

57
Narrow-focus issues include researcher involvement in model support and in technology transfer efforts. Again, these issues are probably not unique to the Pacific Northwest, but cut across regional boundaries. Because this key question addresses rapid

transfer of information, the broad-focus issue noted by the Workgroup is that implementing a national information management system takes too long.

A. Improve documentation and model support by applying product life-cycle methods.

After the difficult tasks of model design, development, and testing are complete, an

entirely new phase of the product life cycle commences. Models developed for

research environments and with research needs in mind often are scant on information about how to apply them. Extensive documentation is required to use

computer models properly. Documentation can include user manuals, tutorials,

online help facilities, bug reports, example applications, technical reports, and

peer-reviewed articles. All this helps to get users started with a model, but additional and ongoing support also needs to be offered. This may include training

sessions for new users, technical support for software installation or to interface

with other applications or data, programming support for bug fixes or special

application needs, and model updates as new research information becomes

available. Support may be made available via telephone or the Internet. These

things, of course, put a tremendous burden on developers and an organization, so

a high level of commitment is necessary to ensure that an information infrastructure

is in place to accommodate these tasks.

B. Standardize and provide desired user interfaces. It is well accepted that, for most

software users, the interface is the application; for example, when we use software

applications, we think very little about how the software is reformatting a paragraph

(in the case of a word-processing application) or how it is calculating a fire-spread

vector for a lOO-square-meter area (in the case of a fire-spread application). We

are thinking, instead, about our particular problem and task and how to get the

application to help us with it. To do that, we need to interact with a user interface

that is, in effect, our sense of the application.

An application must provide an interface (e.g., a graphic user interface) to the user

that is natural to work with-one that mirrors, in some notion, natural ways for the

user to perform important tasks (Schmoldt 1992). Even though working with a

natural and easy-to-use interface is important, it is equally important to have that

same look and feel when working with other, related applications. This is essentially the idea developed and marketed by Apple Computer, Inc., in the 1980s and

subsequently adopted by most other developers of computer operating systems.2

When all applications present a consistent interface to users, the time required for

users to become proficient with a new application is reduced drastically. Neither

consideration is critical for transferring research information, but without them,

results will be more awkward for users and are less likely to be adopted and

applied.

2The use of trade or firm names in this publication is for reader

information and does not imply endorsement by the U.S.

Department of Agriculture of any product or service.

58
C. Explore alternate means for accomplishing data management (e.g., Contracting)

and technology transfer. Information-resources management requires tremendous

organizational commitment, both funding and infrastructure. Such levels of organizational support do not appear overnight and require time to evolve and develop.

Also, supported information resources may not meet the needs of all users. In

certain cases, needs of particular users may be unique and not readily satisfied

with the current or planned information resources of an organization.

These limitations of existing information-resources management mean that other

avenues may need to be investigated to meet the needs. In some cases it may be

more expedient to contract for data collection or data management, rather than

assume large amounts of overhead to accomplish the same tasks inhouse. I n

other cases, model development and support may be readily handled by the

private sector where the necessary resources and expertise already exist. Altemative means to accomplish data management and technology transfer should be

considered before a large investment is made in internal resources.

D. Establish and support a development group. It has been suggested already that

model builders and model users work closely together to build long-term relations.

One particular form of long-term relation is a development group. Rather than

working solely on a singular project of moderate-to-long duration, a development

group can work on multiple projects as part of an ongoing relation. This development group can be formally described and funded or can assume a more informal

collaboration in which only tasks and outcomes are well established. The bond

created as part of a development group provides security and stability for both

developers and users. Developers know that their efforts will be accepted, applied,

and appreciated, because models are designed with needs of the users in mind.

Users are secure in the knowledge that they will get help with their immediate

managerial problem and have the support of a group that they can consult as

future needs arise.

E. Apply free market principles (product development, support, and distribution).

Marketing principles, as applied in the business world, can be borrowed and used

for developing, supporting, and distributing fire-disturbance models. Product

markets have their genesis in a perceived user need. A product developed for this

market attempts to fill that need, and to be attractive to users, it must be distinguishable from other, similar products-distinction can be due to lower price or a

higher quality product or service. This means, first, that model builders must

understand the model users’ needs and must adapt models to managerial application; and second, their model must do something different or better than other

competing models.

One way to distinguish one model from another is through the model support

offered. A good model (or marketable product) will fall into disuse or will disappoint

users if it is not supported. Followup service needs to target questions and problems the user is likely to have, including installation, use, application, extension,

and integration. Models that perform well in this arena will be applied widely and

will establish themselves as valid and essential managerial tools.

5 9
Distributing a model effectively requires, among other things, knowledge of potential users and their applications (i.e., the decisions that they must make). This

information is essential during the model development stage as well. Advocacy, or

testimonial, by a satisfied user is one way that businesses sell a product or service.

This approach can be applied here also. By working closely with an end user

through model development and by supporting delivery and application, that user

will become an advocate for that model to other users. Model developers also can

target professional meetings catering to managerial concerns and applications, and

demonstrations and workshops can be used to introduce a model to the user

community. If actual use and application of a model are important to a developer

(which they should be), then some effort must be directed to promoting and distributing a model, much as a business concern would sell a product.

Question 4: How can we incorporate social and political issues into models and

decision-support systems?

One of the cornerstones of the ecosystem-management paradigm is the incorporation

of social and political knowledge into land management. From a practical standpoint,

this is not a new idea-social and political concerns have influenced land management

for a long time. What is new is that social and political issues must now be validated

and explicit, and they must be considered in concert with biological and physical

components of the landscape. Human interactions are now part of the ecology of a

landscape rather than exogenous to the biophysical ecosystem. Consequently,

models and decision-support tools must have mechanisms allowing for the incorporation or consideration of social and political issues.

At the state level, models should be useful in supporting state regulations. At the

national level, it should be possible to incorporate congressional and agency policy

into models. Also at the national level, there should be compatibility and comparability

of analysis outputs across regions.

A. Incorporate sociological research when developing decision-support systems.

Recent and extensive sociological research is beginning to understand and explain

many of the cultural, political, and economic impacts of human populations. These

impacts have modified landscape use and appearance over time and will continue

to do so. As fire-disturbance models deal with large spatial and temporal scales,

there is a need to include timely sociological research into models to account for

human influences on land use. Otherwise, a very significant determinant of landscape change is ignored by those models. Not only direct human impacts on the

landscape should be considered but also public preferences and perceptions

regarding fire. Human understanding and tolerance for fire disturbances and

effects might be included in models that deal with suppression, fuels management,

smoke management, or prescribed burning.

B. Modelers and managers must be aware of emerging issues and anticipate future

concerns. In addition to current social issues and their influences on modeling and

decision support, there needs to be awareness of emerging issues-changes in the

way that the human population interacts with the natural environment. Public interests, demands, and perceptions change much more frequently and unpredictably

60
than biophysical phenomena. It is important to anticipate those changes where

possible and react quickly and intelligently to them. In the future, there will be new

social issues that we currently are not aware of, and that we currently are unable to

assess from a modeling perspective. Based on recent sociological trends, however,

we should develop models that can adjust to social changes, much like current firebehavior models can be modified to deal with a variety of fuel conditions.

Question 5: How can relevant interdisciplhry resource management issues be

incorporated into models?

The previous question on social concerns is closely tied to ecosystem management,

and hence it received special treatment. It also can be viewed as a special case of the

general question that deals with incorporating interdisciplinary resource-management

issues into models; that is, how can we incorporate issues from diverse resources,

such as wildlife, soils, water, timber, fisheries, and recreation, into our large-scale firedisturbance models? There are few specific answers that the Workgroup can offer to

this question. Exact details will differ by situation. The following responses provide

general guidance, however, on how to address interdisciplinary issues.

A number of resource issues specific to the Pacific Northwest were mentioned by the

Workgroup as important:

l Interaction of fire with threatened and endangered species of regional concern

(e.g., northern spotted owl [Strix occidentalis caurina])

l Protection of coarse woody debris in streams and rivers

l Old-growth sustainability

l Air quality with respect to human health

l Class 1 wilderness area visibility

l State smoke management plans

l Water quality

Broader issues of concern to the Workgroup were (1) interaction of fire with threatened

and endangered species, (2) regional haze generation and mitigation, and (3) effects

of fire on carbon balance.

A. Open communication between modelers and users. The frequency with which the

idea of communication has been reiterated throughout this Workgroup report attests

to its importance. Modelers and users should communicate openly about interdisciplinary issues-data available for various resources, influence on and by fire

disturbance, and managerial decisionmakig needs. Not all interdisciplinary issues

have equal importance or good data availability. Modelers should select those

interdisciplinary issues that have high importance for managers and for which good

data exist.

6 1
Synopsis

6 2

B. Involve a cross-section of managers and policymakers in model development. It

should be obvious that the interdisciplinary nature of resource-management issues

demands that a cross-section of resource managers be involved. This ensures that

each discipline is included adequately, and that cross-cutting issues are properly

addressed by knowledgeable representatives from each subject area. Because

decisionmaking needs of policymakers differ from those of managers, both types

of disciplinary specialists should be included.

C. Assign responsibility, develop measurement criteria, monitor accomplishments,

and provide accountability for both research and management. A number of fairly

specific things can be done to help incorporate interdisciplinary issues in models:

(1) responsibilities for data collection or issue identification and description should

be assigned to someone; (2) measurement criteria should be defined to establish

what aspects of, and to what extent, a discipline is incorporated into a model;

(3) research and management should periodically monitor accomplishments to

determine whether work is progressing satisfactorily; and (4) both developers and

users should be accountable for their tasks and for the overall capabilities and

application of the model. Because of the number of different specialties involved

with an interdisciplinary modeling project, it is particularly important that everyone

have clearly defined and monitored tasks with well-established standards for

success.

In general, the needs addressed by this Workgroup include building more accurate,

more inclusive, and more useful models, integrating models into decision-support

tools, improving communication, and strengthening relations between management

and research. Models need to have increased flexibility to cover a broad range of

vegetation, fuels, climate, and topography. They also need to include additional

aspects of fire behavior, such as lightning strikes, crown-fire ignition, and crown-fire

spread. To assist with decision support, modelers and users must communicate

effectively in developing models that address current management issues, such as

social and political needs and biodiversity concerns.
ANALYTICALMETHODSANDREWLTS

Analysis of

Priority Vectors

Pairwise comparisons by Workgroup members allowed us to generate priority vectors

for the items being compared by using the principal right eigenvector method of Saaty

(1980). These priorities may be for either “importance” or “practicality.” Within a

Workgroup, all corresponding judgments by Workgroup members were geometrically

averaged to produce a single judgment for each comparison. This produced a group

priority vector. But two questions could be asked about the final priority vectors: (1)

Was there general agreement among Workgroup members on the rankings in the

priority vector? and (2) Are different priority values in a priority vector really different?

Answers to these questions could have a significant bearing on how the final rankings

will be used to direct research on large-scale fire disturbance.

The individual judgments used to create a group priority vector can be treated as

samples from a population of experts that are independent and identically distributed.

Given that, priority vectors can be generated separately from the judgments of each

Workgroup member. The resulting sample of priority vectors can then be analyzed

statistically to answer the above questions.

Individual judgments are taken from the set [l, 2, . . . . 91 and their reciprocals. We can

assume that this constitutes a truncated log-normal distribution (Basak 1990, Crawford

and Williams 1985, de Jong 1984), or some other distribution, (e.g., gamma [Vargas

1982, Zahedi 1986]), and then perform the necessary calculations to determine the

distribution of the principal right eigenvector, which is the priority vector. This, however, locks in assumptions about the distribution of individual judgments and can result

6 3
in complicated statistical tests. Alternatively, we can assume that final priority vector

elements are distributed normally and perform an analysis of variance (ANOVA) with

posthoc tests for mean differences. One would not necessarily expect vector elements

to be normally distributed, however; in fact, with a small sample size, normality tests

are not very convincing. The third alternative, and the one chosen here and used by

Smith and others (1995), is to apply distribution-free tests analogous to tests based on

the normal distribution of vector elements. The drawback is that distribution-free tests

are conservative and may fail to detect significant differences.

Each of the following three tests ranks the data before calculating statistics, so relative

magnitude information is lost (SYSTAT 1992). This constitutes the conservative nature of

these tests. The Friedman two-way ANOVA test analyzes the rankings by the different

Workgroup members for each set of items being compared (key questions or response, in

our case). The null hypothesis is that there is no systematic variation in the rankings

across items by Workgroup members. The Kruskal-Wallis one-way ANOVA test indicates

whether there are differences between the priority vector elements (i.e., key questions or

responses), taking into account judgments of all Workgroup members. The null hypothesis is that there are no differences. Although this test identifies that differences exist, it

does not specify which vector elements are different.

Analysis of

Rankings

Links Among Fire

Effects, Fuels, and

Climate

The Wilcoxon signed-ranks test indicates which pairs of priority vector elements are

different. A pairwise table of probability values can be created that is equivalent to an

ANOVA posthoc test for mean differences. This test may not provide conclusive

results in all cases. This occurs for three reasons: (1) the Kruskal-Wallis test calculates probability values based on a chi-square approximation, and the Wilcoxon

signed-ranks test uses a normal approximation-so while the former may indicate a

statistically significant result, the latter may not confirm any differences in the pairwise

tests; (2) some mathematical precision is lost because ranks are used rather than

actual data values; and (3) poor agreement on rankings by Workgroup members will

mask differences among individual responses. Results from these statistical tests

nevertheless can discern some important differences in rankings. Analyses and

conclusions by the workgroups appear in the following sections.

The following analyses examine rankings of importance for the key questions and of

importance and practicality for the responses to each key question. For each type of

ranking (importance or practicality), we applied the distribution-free statistical tests

described previously to (1) determine how well Workgroup members agreed on their

rankings of key questions or responses, (2) determine whether differences between

rating scores for the key questions or responses were significant, and (3) identify

which key questions or responses differed significantly. The next sections analyze

importance and practicality separately.

Importance rankingsKey question+Six Workgroup members compared the five key questions appearing

in table 2 for importance. A Friedman two-way ANOVA test rejected the null hypothesis (p = O.OOl), indicating that judgments of Workgroup members differed systematically; i.e., there was good agreement on the rankings of key question importance

across Workgroup members. A Kruskal-Wallis test for differences of mean rating

64
scores for the key questions also was highly significant (p c 0.0005), suggesting that

real differences existed among the rating scores. A Wilcoxon signed-ranks test

produced a matrix of pairwise probabilities (table 6) that indicated which of the key

question importance scores in table 2 may actually be different. The highest ranked

key question (factors [0.38]) was significantly more important than each of the other

key questions, and the second highest ranked question (knowledge of fire [0.25]) was

different from the two lowest-ranked key questions. There is no evidence to suggest

that the two lowest-ranked key questions (management importance [0.08] and landscape [O.ll]) were significantly different. This produced a three-level scale of importance for these key questions-with one question at the top, two at the bottom, and

two questions between the others.

Response-The number of responses differed with each key question. Also, for

question 3, dealing with scales, only five Workgroup members were able to provide

judgments. Statistical tests, similar to those conducted for the key questions, were

performed for each set of responses. Results for the Friedman and the Kruskal-Wallis

tests, which were applied to the responses of each key question, appear in table 7. Only

for the landscape key questions was there evidence to indicate good agreement by

Workgroup members regarding rankings of the respective responses. Lack of agreement

for the responses to the other three key questions obscured individual response differences detected by subsequent tests. Still, for management importance and landscape

key questions there seemed to be significant differences among rating scores for the

different responses, as indicated by the Kruskal-Wallis test probability values.

Despite the conservative and approximate nature of these tests, a few differences are

apparent from the probability matrices in tables 8 through 10. The highest ranked

response (knowledge of fire [0.16]) for key question 2 (table 8) appears to rate as

significantly more important than the lowest three responses, but otherwise there is

little statistical evidence to say that the Workgroup was able to distinguish differences

among these responses. No significant differences were detected among the responses to question 3, owing most likely to the lack of agreement by Workgroup

members, as is apparent from table 7. On the other hand, Workgroup judgments were

very consistent for key question 4 (table 9). For this question, the highest ranked

response (engineer [0.28]) differed from the four lowest ranked responses. With

regard to management links, key question 5, the two highest ranked responses (public

[0.17] and knowledge for management [O.lq) differed from the three lowest ranked

responses (table 10). Again, it should be emphasized that lack of agreement on

judgments by Workgroup members for each set of responses led to importance ratings

with a fairly narrow range after averaging. This resulted in few significant differences

across ratings for each set of responses.

Practicality rankings-As noted above, the key questions were not compared for

practicality, and no comparisons were made for the responses to question 1. All six

Workgroup members compared the responses to questions 2-4, but only four members

were able to make practicality comparisons for key question 5. Statistical tests, similar

to those conducted for the key question responses with respect to importance, were

performed. Results for the Friedman and Kruskal-Wallis tests appear in table 11. Only

for the management importance key question was there strong evidence to indicate

6 5
good agreement by Workgroup members regarding rankings of the responses (Friedman test). There also was reasonably good agreement for the scales key question

(0.083). For those same two key questions, there appear to be significant differences

among rating scores for the different responses, as indicated by Kruskal-Walks test

probability values.

There were no apparent differences among responses to key question 2, due in large

part to the lack of agreement by Workgroup members. When we examined the

Wilcoxon signed-ranks test for scale, the highest ranked response seemed to be

different from the three lowest ranked responses at p = 0.68 (table 12). For question

4, adjacency, the highest ranked response for practicality, appeared different from two

of the lower ranked responses, fire regime and fire breaks (table 13). Additional

differences, however, were masked by low consistency scores. In the links to management key question, the Workgroup felt that the two highest ranked responses,

public and knowledge for management, are more easily attained than any of the other

responses (table 14). This strong result reflects the high level of consistency in

Workgroup judgments, which is highlighted statistically in table 11.

Table 6-Probability values generated by the Wilcoxon signed-ranks test for

differences across means of the importance-rating scores for the key questions

related to links among fire effects, fuels, and climate

Management

Key question” importance Knowledge

Management

importance 1 .ooo

Knowledge .028 1 .ooo

Factors .028 .068

Landscape .249 .028

Scales .028 .173

‘See table 2 for a complete description of each key question.

Factors

1 .ooo

,028

.028

Landscape Scales

1 .ooo

.225 1 .ooo

Table ‘I-Probability values for agreement on importance rankings (Friedman)

and differences in mean rating scores (Kruskal-Wallis) for key questions related

to links among fire effects, fuels, and climate

Key question”

Factors

Knowledge

Scales

Landscape

Management importance

Friedman test probability Kruskal-Wallis probability

- -

0.143 0.115

.833 .868

.020 .007

.179 .048

a See table 2 for a complete description of each key question.

6 6
Table &-Probability values generated by a Wilcoxon signed-ranks test for differences

across means for the importance-rating scores of responses to key question 2 (What

ecological role does fire play at larger scales?) for links among fire effects, fuels, and

climate

Responses to key question 2”

Responses

to key Intense, Know

question 2” Synoptic Records Preserve Fuels severe fire

Synoptic 1 .ooo

Records .917 1 .ooo

Preserve .463 .666 1 .ooo

Fuels .345 .463 .345 1 .ooo

Intense, severe .463 .345 ,345 .753 1 .ooo

Know fire .917 .500 .345 .249 .068 1 .ooo

Propagation .116 .046 .116 .686 .500 .043

Ignition .046 .046 .116 .463 .686 .043

Fire history .463 .463 .249 .917 .686 .043

n See table 2 for a complete description of responses to key question 2.

Propa- Fire

gation ignition history

1 .ooo

.500 1 .ooo

.465 .500 1 .ooo

Table g-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 4 (What are the critical characteristics of the fire-behavior environment?) for links among fire effects, fuels, and climate

Responses to key question 4”

Responses

to key Fire Linked Fuel Predict

question 4” breaks processes Adjacency mgmt. Engineer regime Procedures

Fire breaks 1 .ooo

Linked

processes .028 1 .ooo

Adjacency .600 .173 1 .ooo

Fuel mgmt. .075 .345 .043 1 .ooo

Engineer .046 .173 .068 .345 1 .ooo

Predict regime .173 .463 .345 .138 .043 1 .ooo

Procedures .345 .345 .500 .138 .043 .893 1 .ooo

aSee table 2 for a complete description of responses to key question 4.

67
68

,

Table 10-A Wilcoxon signed-ranks test generates a matrix of probability values for

differences across means for the importance-rating scores of responses to key question 5

(What links are important to management?) for links among fire effects, fuels, and climate

Responses to key question 5”

Responses

to key Techno- Biotic Severity Fire Know

question 5” Enabling lOcfY response measure regime mgmt. Emissions Smoke Public

Enabling 1 .ooo

Technology .116 1 .ooo

Biotic

response 500 .116 1 .ooo

Severity

measure .686 .138 .715 1 .ooo

Fire regime .345 .116 1 .ooo .893 1 .ooo

Know mgmt. .753 .043 .345 .600 .345 1 .ooo

Emissions .173 .600 .463 .116 .173 .028 1 .ooo

Smoke .173 .463 .249 .173 .116 .028 .285 1 .ooo

Public .753 .043 .249 .500 .249 .249 .028 .028 1 .ooo

‘See table 2 for a complete description of responses to key question 5.

Table 11-Probability values for agreement on importance rankings (Friedman)

and differences in mean rating scores (Kruskal-Wallis) of responses to key

questions with respect to practicality rankings for links among fire effects, fuels,

and climate

Key question* Friedman test probability Kruskal-Walks probability ~-

Factors - -

Knowledge 0.356 0.531

Scales ,083 .040

Landscape

Manaaement importance .OOl .ooo

a See table 2 for a complete description of responses to key questions.
Table 12-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 3 (At what scales are processes important?) for links among fire

effects, fuels, and climate

Responses to key question 3”

Responses Decision

to key support Multiple Ecological Landscape

question 3” Climate tool scales data Explanatory Errors scale -_

Climate 1 .ooo

Decision

support tool .144 1 .ooo

Multiple scales .066 1 .ooo 1 .ooo

Ecological data .066 .715 .715 1 .ooo

Explanatory .273 .273 .068 .109 1 .ooo

.465 .273 .144 .285 .655 1 .ooo

Landscape level .088 .273 .068 .068 .273 1 .ooo 1 .ooo

#See table 2 for a complete description of responses to key question 3.

Table 13-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 4 (How are links related in a landscape context?) for links among fire

effects, fuels, and climate

Responses to key question 4”

Responses

to key Fire Linked Fuel Predict

question Q breaks processes Adjacency mgmt. Engineer regime Procedures

Fire breaks 1 .ooo

Linked processes .686 1 .ooo

Adjacency .043 .463 1 .ooo

Fuel mgmt. .893 .686 .116 1 .ooo

Engineer .893 .893 .144 .686 1 .ooo

Predict regime .138 .249 .043 .116 .138 1 .ooo

Procedures .345 .345 .116 .500 .345 .463 1 .ooo

‘See table 2 for a complete description of responses to key question 4.

69
Table 14-Probability values generated by a Wilcoxon signed-ranks test for differences

across means for the importance-rating scores of responses to key question 5 (What links

are important to management?) for links among fire effects, fuels, and climate

Responses to key question 5”

Responses

to key Biotic Severity Fire Know Emisquestion 58 Enabling Technology response measure regime mgmt. sions Smoke Public

Enabling 1.000

Technology .686 1 Boo

Biotic response .715 .080 1 .ooo

Severity measure .753 .753 .345 1 .ooo

Fire regime .917 .345 .249 .593 1 .ooo

Know mgmt. .028 .028 .028 .028 .028 1.000

Emissions ,345 .116 .075 .173 .116 .028 1.000

Smoke .463 .600 .116 .345 .225 .028 .109 1 .ooo

Public .028 .046 .028 .028 .028 .225 .075 .075 1.000

‘See table 2 for a complete description of responses to key question 5.

Conclusions-Knowing the factors important to fire disturbance and knowing the links

between them seem to be substantially more important than the other key questions

(table 6). Key question 5, links important for management, on the other hand, is the

least important-owing perhaps to the current lack of fundamental scientific knowledge

about the important factors and their links; i.e., because the science contains large

gaps, management issues cannot be intelligently addressed and, hence, are secondary.

Fire as a Large-Scale

Disturbance

Aside from particular exceptions noted above, responses within each key question

were difficult to rank by importance or practicality. This most likely was due to the

number of items being ranked (seven or nine in each case) and to the relative lack of

consistency in judgment among Workgroup members (tables 7 and 11). While each

Workgroup member’s judgments were internally consistent, there was little agreement

among members. This level of nonagreement strongly corroborates the feeling that

links among fire effects, fuels, and climate are poorly understood and should be an

important focus for future research and expanded modeling efforts.

The following analyses examine rankings of importance for the key questions, for the

focused questions under each key question, and for the responses to selected focused

questions. In each case, we applied the distribution-free statistical tests described

previously to (1) determine how well Workgroup members agreed on their rankings of

questions or responses, (2) determine whether differences among rating scores for the

questions or responses were significant, and (3) identify which questions or responses

differed significantly. The next sections analyze separately the importance for the key

questions, the focused questions, and the responses.

70
Key questions-Five Workgroup members compared the four key questions appearing in table 3 with regard to importance. A Friedman two-way ANOVA test failed to

reject the null hypothesis (p = 0.115) indicating that judgments of Workgroup members

may not differ systematically; i.e., there was no statistical evidence to say that good

agreement exists for the rankings of key question importance across Workgroup

members. Despite this lack of significant agreement, however, a Kruskal-Wallis test

for differences of mean rating scores for the key questions was significant (p = 0.052)

suggesting that real differences existed among the rating scores for the different key

questions. A Wilcoxon signed-ranks test produces a matrix of pair-wise probabilities

(table 15) indicating which of the key question importance scores in table 3 may

actually be different. There is some statistical evidence to suggest that the highest

ranked key question (dynamics [0.41]) was significantly more important than the two

lowest ranked key questions (p = 0.068 in each case). The second ranked key

question (ecological [0.28]) fell in between the others and cannot be distinguished

as significantly different from any of the other key questions.

Focused questions-The number of focused questions differed with each key

question. Statistical tests, similar to those conducted for the key questions, were

performed for each set of focused questions. Results for the Friedman and the

Kruskal-Wallis tests applied to the focused questions of each key question appear in

table 16. Only for the dynamics key question was there any evidence to indicate some

agreement by Workgroup members regarding rankings of the focused questions

(p = 0.074). Lack of agreement on the focused questions of the other three key

questions obscures individual response differences that could be detected by subsequent tests. For the dynamics key question there appeared to be a significant difference among rating scores for the different focused questions, as indicated by the

Kruskal-Wallis test probability value (p = 0.041).

Due to the lack of Workgroup agreement on rankings, no significant differences could

be identified among the focused questions for key questions 2 and 4. The most

consistent rankings occurred for the focused questions in key question 1. Pairwise

probability values for mean differences appear in table 17. Here, the lowest ranked

focused question, stochastic, appears to be significantly different from the remaining

ones, except refugia, which is the second lowest one. In key question 3, the highest

ranked focused question, fragmentation, appears to be different from the two lowest

ranked questions, political and landscape (table 18).

Responses-It is apparent that there was little agreement among Workgroup members

regarding the importance of responses to the most important focused questions (table

19). Only for the second focused question, public, under the managed key question

was there good agreement and were differences among response ratings significant.

The highest ranked response, aesthetics, appeared to be different (table 20) from all

the other responses, except perceptions, the second highest response. Similarly, the

lowest ranked response, safety, appears relatively different from the two highest

ranked responses. No other differences were significant.

71
Table 16-Probability values generated by the Wilcoxon signed-ranks test for

differences across means of the importance-rating scores for the key questions

related to fire as a large-scale disturbance

Key questions”

Key questions” Ecological Managed Dynamics Fire behavior

Ecological 1 .ooo

Managed .465 1 .ooo

Dynamics .273 .068 1 .ooo

Fire behavior .I38 .500

’ See table 3 for a complete description of key questions.

.068 1 .ooo

Table 16-Probability values for agreement on importance rankings (Friedman)

and differences in mean rating scores (Kruskal-Wallis) for key questions for fire

as a large-scale disturbance

Key question” Friedman test probability

Dynamics 0.074

Ecological .861

Kruskal-Wallis probability

0.041

.537

Managed .401 .384

Fire behavior .350 .126

’ See table 3 for a complete description of key questions.

Table 17-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 1 (What are the critical aspects of spatial and temporal dynamics of fire

at large scales?) for fire as a large-scale disturbance

Responses to key question la

Responses to

key question 1 a Modeling Stochastic Refugia Climate Impact Heterogeneity

Modeling 1 .ooo

Stochastic .043 1 .ooo

Refugia .068 .465 1 .ooo

Climate .893 .080 .080 1 .ooo

Impact .I44 .043 .I44 .080

Heterogeneity .144 .068 .225 .I38

* See table 3 for a complete description of responses to key question 1.

1 .ooo

.686 1 .ooo

72
Table l&-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 3 (How can fire be managed at large scales?) for fire as a large-scale

disturbance

Responses to key question 38

Responses to Management

key question 38 Landscape objectives Public Political Fragmentation

Landscape 1 .ooo

Management

objectives .465 1 .ooo

Public .225 .893 1 .ooo

Political .345 .686 .500 1 .ooo

Fraamentation .080 .686 .893 .043 1 .ooo

. See table 3 for a complete description of responses to key question 3.

Table 19-Probability values for agreement on Importance rankings (Friedman)

and differences in mean rating scores (Kruskal-Wallis) for the most important

focused questions within each key question for fire as a large-scale disturbance

Focused question* Friedman test probability Kruskal-Wallis probability

Dynamics 1 0.787

Dynamics 2 .779

Ecological 1 .406

Ecological 2 .919

Managed 1 .739

Managed 2 .067

Managed 3 .196

Fire behavior 1 .247

Fire behavior 2 .770

0.690

.885

572

.863

.153

.018

.095

.133

.572

* See table 3 for a complete description of key questions and focused questions.

7 3
Table PO-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to

focused question 2 (What characteristic of a fire regime has the most importance to the public?) under key question 3 (How can fire be managed at large

scales?) for fire as a large-scale disturbance

Response to focused question 28

Responses to

focused question 2” Aesthetics Safety Perceptions Acceptable Smoke

Aesthetics 1 .ooo

Safety .068 1 .ooo

Perceptions .144 .225 1 .ooo

Acceptable .043 .715 .465 1 .ooo

Smoke .068 .068 .893 .138 1 .ooo

’ See table 3 for a complete description of responses to focused question 2.

Conclusions-Although the Workgroup offered few accordant and specific recommendations regarding research on fire as a large-scale disturbance, we suggest that the

most important first step is to develop higher resolution methods of assessing temporal

and spatial dynamics. This can be accomplished initially by improvement of existing

fire-effects models. The large number of questions secondary to the initial key questions suggests that there are many facets to assessing large-scale fire-disturbance

effects. The temporal and spatial dynamics of large fires are mostly unknown, particularly as they relate Zo fire behavior in complex topography. This clearly limits our ability

to understand the ecological effects of large fires and to deal with them from a management perspective.

Fire-Effects Modeling

Structures

To ease the task of making comparisons, the Workgroup logically divided the 10 key

questions into two subcategories, one addressing model structure and application

issues and the other, model data collection and use. Key questions within each

subcategory were compared pairwise for importance only, and then the two subcategories, themselves, were compared for importance. Then, multiplying the priority

values for each key question in each subcategory by the priority values of its subcategory produced global priority values for the key questions. As in the other primary

topics, distribution-free statistical tests were used to discern differences in rankings

and to identify where Workgroup members agreed in their rankings.

Importance rankingsKey questions-Table 4 lists aggregated ratings for the key questions and responses.

We performed a Friedman two-way ANOVA test to discern differences in rankings for

the key questions across the six Workgroup members. The Friedman test rejected the

null hypothesis (p = 0.002), indicating that judgments of Workgroup members differed

7 4
systematically; i.e., there was good agreement on the rankings across Workgroup

members. A Kruskal-Wallis test for differences of mean rating scores for the key

questions was also highly significant (p = 0.001) suggesting that real differences

existed among the rating scores for the different key questions. A Wilcoxon signedranks test produced a matrix of pairwise probabilities (table 21) indicating which of the

key question importance scores in table 4 may actually be different.

The highest ranked key question, validation, seemed to be significantly more important

than most of the remaining key questions. It was not different, however, from the next

two highest ranked key questions, scale issues and model outputs. Similarly, the

second highest ranked key question, scale issues, was significantly different from the

six lowest ranked key questions. Although the third highest ranked key question,

model outputs, has a relatively high aggregate score (0.14), significant differences

from lower scores are not substantiated owing to the highly variable ratings for that key

question by Workgroup members. For many of the remaining key questions, few

patterns of significant difference can be claimed. Overall agreement in rankings was

supported by the Friedman test, but there were instances where excessive variation in

ratings (and rankings) obfuscated more meaningful results.

ResponseeResponses were generated for 6 of the 10 key questions as workshop

time allowed. Five of these six constitute the most important questions. A statistical

examination of ratings by Workgroup members for responses within each key question

appears in table 22. There was good agreement (Friedman test) by Workgroup

members on response rankings for three of the six key questions, including the two

most important ones from table 4, validation and scale issues. For each of these three

key questions where agreement was high, Kruskal-Wallis tests for mean differences in

rating scores for the responses also were very significant. For the other key questions

where there was less agreement among Workgroup members, mean differences were

less statistically significant.

Wilcoxon signed-ranks tests provide details about specific differences among response ratings for the key questions. We looked only at those key questions where

Workgroup members had good agreement in rankings (from table 22). For key question 1, validation, we found that the lowest ranked response, bounds, was very different from each of the other responses (table 23). The second lowest ranked response,

compare to models, was different from the two highest ranked responses and from the

lowest one. The two highest ranked responses, sensitivity and compare to data, were

both different from the two lowest ranked responses. So, for this key question there

seems to be a definite, high-importance group of two responses, a low-importance

response, and two responses that fall in the middle. For key question 2, scale issues,

there were few really strong differences among responses (table 24) owing to their

number (eight) and their relatively similar magnitudes. The lowest ranked response,

minimize errors, was different from most other responses. The highest ranked, fine

resolution, was different from many of the lowest ranked responses, but not from the

second lowest ranked one, quantify error. For key question 5, scale effects, the two

responses were significantly different (p = 0.038).

7 5
Practicality rankings-Only responses to key questions were rated for practicality.

For the six key questions listed in table 25, only two, scale issues and calibration, had

significant (p < 0.05) Friedman test values, indicating Workgroup agreement on

rankings. Even though the Kruskal-Wallis test for key question 1, validation, produced

a relatively significant probability score, the lack of agreement across Workgroup

members caused most pairwise Wilcoxon signed-ranks tests to be not significant. A

table of those values therefore is not provided here.

For the responses to key questions 2, scale issues, the least important response was

also the least practical and seemed to be significantly less practical (table 26) than

most of the other responses. The two most practical responses, variability and resolution and extent, were significantly different from the three least feasible ones. Because

the practicality ratings in table 4 do not differ drastically for this key question, few of the

other responses can be judged as different from either the most or least practical

responses. For key question 4, calibration, the most important response, components,

also was the most practical, and there was some statistical evidence to suggest that

the practicality rating score for components was different from the three least practical

responses (table 27). One of the least practical responses, domain, seems to be

different from several of the more practical responses. Because the Kruskal-Wallis

test was not highly significant (p = 0.06) for key question 4, there are few specific

differences that can be inferred from the results.

Table 21-Probability values generated by the Wilcoxon signed-ranks test for differences

across means of the importance-rating scores for the key questions related to fire-effects

modeling structures

Key questions”

Model

Scale compo- Calibra- Scale Model Data Data

Key questions” issues nents tion Validation Climate effects Structure outputs available tools

Scale issues 1.000

Model components .043 1 .ooo

Calibration .141 .173 1.000

Validation .249 .046 .043 1.000

Climate .028 344 .046 .028 1.000

Scale effects .028 .345 .500 .028 .116 1.000

Structure .027 .786 .027 .028 .600 .207 1.000

Model outputs .917 .043 .248 .345 .116 .279 .115 1.000

Data available .046 .833 .248 .028 .916 .345 .528 .144 1.000

Data tools .027 .293 .027 .028 .598 .046 .I36 .116 .414 1.000

B See table 4 for a complete description of key questions.

7 6

F””
Table 22-Probability values for agreement on importance rankings (Friedman)

and differences in mean rating scores (Kruskal-Wallis) for the 6 most important

key questions for fire-effects modeling structures

Key questions” Friedman test probability

Validation 0.008

Scale issues .025

Model outputs .130

Calibration ,419

Scale effects .041

Structure .246

’ See table 4 for a complete description of key questions.

Kruskal-Wallis probability

0.004

.014

.085

.083

.004

.202

Table 23-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 1 (How does one validate a model’s structure with respect to error

propagation?) for fire-effects modeling structures

Responses to Compare

key question 1 a to data

Responses to key question la

Compare Compare

to models structure Sensitivity Bounds

Compare to data 1 .ooo

Compare to models .080 1 .ooo

Compare structure .028 .043 1 .ooo

Sensitivity .688 .048 .028 1 .ooo

Bounds .500 .249 .028 .893 1 .ooo

’ See table 4 for a complete description of responses to key question 1.

C‘””
Table 24-Probability values generated by a Wilcoxon signed-ranks test for differences across

means for the importance-rating scores of responses to key question 2 (What are the relevant

spatial and temporal scale Issues [including extent and resolution] related to modeling fire

effects?) for fire-effects modeling structures

Responses to key question 2’

Responses to Resolution Minimize Quantify Small Fine Temporal

key question 2” and extent error error Variability scales resolution aggregation Structure

Resolution

and extent 1 .ooo

Minimize error .046 1 .ooo

Quantify error .249 .666 1 .ooo

Variability .917 .026 .345 1 .ooo

Small scales .463 .046 .600 .136 1 .ooo

Fine resolution .345 .026 .I16 .249 .026 1 .ooo

Temporal

aggregation .753 .046 .345 .693 .345 .075 1 .ooo

Structure .075 .249 .753 .249 .917 .046 .500 1 .ooo

a See table 4 for a complete description of responses to key question 2.

Table 26-Probability values for agreement on practality rankings (Friedman)

and differences in mean rating scores (Kruskal-Wallis) for the 6 most important

key questions for fire-effects modeling structures

Key question” Friedman test probability Kruskal-Wallis probability

Validation 0.212 0.064

Scale issues .OlO .008

Model outputs .950 .928

Calibration .045 .061

Scale effects 1 .ooo 1 .ooo

Structure .849 .407

g See table 4 for a complete description of key questions.

78
Table 26-Probability values generated by a Wilcoxon signed-ranks test for differences across

means for the practicality-rating scores of responses to key question 2 (What are the relevant

spatial and temporal scale issues [including extent and resolution] related to modeling fire

effects?) for fire-effects modeling structures

Resoonses to kev question 2’

Responses to Resolution Minimize Quantify Small Fine Temporal

key question 2” and extent error error Variability scales resolution aggregation Structure

Resolution

and extent 1 .ooo

Minimize error .028 1 .ooo

Quantify error .028 .088 1 .ooo

Variability .600 .028 .028 1 .ooo

Small scales .116 .075 .917 .345 1 .ooo

Fine resolution .046 .138 .345 .173 .917 1 .ooo

Temporal

aggregation .173 .075 .400 .043 .600 .917 1 .ooo

Structure .116 .028 .138 .463 .753 .686 .345 1 .ooo

a See table 4 for a complete description of responses to key question 2.

Table 27-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the practicality-rating scores of responses to key

question 4 (How does one calibrate a fire-effects model?) for fire-effects modeling structures

Responses to key question 4”

Responses to Against Against Against

key question 4a data model theory Consistency Components Domain

Against data 1 .ooo

Against model .043 1 .ooo

Against theory .138 .465 1 .ooo

Consistency .463 .116 .116 1 .ooo

Components .116 .917 .463 .116 1 .ooo

Domain .753 .116 .043 .068 .028 1 .ooo

’ See table 4 for a complete description of responses to key question 4.

79
Conclusions-At the level of key questions, Workgroup members were highly consistent in their rankings of importance. Due to the broad nature of the key questions and

the many tasks needed to address them, however, Workgroup members felt ill-equipped

to provide reasonable judgments about practicality. Key questions dealing with

validating model structure and incorporating relevant spatial and temporal scales for

fire effects received high importance rankings by the Workgroup as a whole. There

also was good agreement by the Workgroup on the relative importance of responses

to these two key questions, and some significant differences between the highest and

lowest ranked responses are apparent. Combining importance rankings and practicality

rankings for responses to the second most important key question seemed to indicate

that the second and third most important responses (variability and resolution and

extent) also were quite practical. This has important implications for research program

direction.

Managerial Concerns,

Applications, and

Decision Support

The following analyses examine rankings of both importance and practicality for the

key questions and for the responses to each key question. For each type of ranking

(importance or practicality), we applied the distribution-free statistical tests described

previously to (1) determine how well Workgroup members agree on their rankings of

key questions or responses, (2) determine whether there were significant differences

among rating scores for the key questions or responses, and (3) identify which key

questions or responses were significantly different. The next sections analyze importance and practicality separately.

Importance rankingsKey questions-Six Workgroup members compared the five key questions appearing

in table 5. A Friedman two-way ANOVA test rejected the null hypothesis (p < 0.0005),

indicating that judgments of Workgroup members differed systematically; i.e., there

was good agreement on the rankings across Workgroup members. A Kruskal-Wallis

test for differences of mean rating scores for the key questions also was highly significant

(p c 0.0005), suggesting that real differences exist among the rating scores. A

Wilcoxon signed-ranks test produced a matrix of pairwise probabilities (table 28)

indicating which of the key question importance scores in table 5 may actually be

different. There did not seem to be any evidence to suggest that the two highest

ranked key questions (model structures [0.43] and communication [0.28]) were significantly different. These two key questions did differ significantly, however, from the

other three questions. The third highest ranked key question (information transfer

[0.15]) also appeared to be significantly different from the two lowest ranked questions

(relevance [0.06] and socialpolitical [0.07]). Consequently, there seem to be three

significant levels of importance for these key questions-with two questions at the top,

two at the bottom, and the fifth question lying between the others.

Response-The number of responses differed with each key question. Again,

however, six Workgroup members compared responses for each question. Statistical

tests, similar to those conducted for the key questions, were performed. Results for

the Friedman and Kruskal-Wallis tests applied to the responses of each key question

appear in table 29. Only for the most important and least important key questions is

there evidence to indicate good agreement by Workgroup members regarding rankings

8 0
of the respective responses. Lack of agreement for the responses to the other three

key questions obscures any individual response differences detected by the subsequent

tests. For each key question there seemed, however, to be significant differences among

rating scores for the different responses, as indicated by Kruskal-Wallis test probability

values.

A few significant differences are apparent from the probability matrices in tables 30-32.

The highest ranked response for key question 1, fire regimes, appears to rate significantly different than the other three responses (table 30). While Kruskal-Wallis tests

for key questions 2 and 4 (each key question having only two responses) showed a

significant difference among their respective responses, Wilcoxon signed-ranks tests

for differences of means failed to be significant-owing most likely to the different

approximations that the two tests employ. For key question 3, the judgments were not

entirely consistent across Workgroup members (table 29) so although overall means

for each response showed significant differences, individual comparisons were less

significant (table 31) because counts of rank differences were mixed. Judgments for

responses to key question 5 (the least important one) were highly consistent. This

allowed any rating differences to be easily picked up by the other tests. All three

responses appear to be significantly different from each other (table 32).

Practicality rankingsKey questions-For practicality comparisons, only five Workgroup members analyzed

the five key questions appearing in table 5. A Friedman two-way ANOVA test marginally

rejected the null hypothesis (p = 0.057) indicating that Workgroup members tended to

agree on their rankings. A Kruskal-Wallis test for differences of mean rating scores for

key-question practicality was significant (p =0.017), suggesting that real differences

exist among the rating scores. A Wilcoxon signed-ranks test produced a matrix of

pairwise probabilities (table 33) indicating which of the practicality scores in table 5

may actually be different. The highest ranked key question for practicality (communication [O&I]) was significantly different from the two lowest ranked questions (relevance

[0.13] and sociopolitical [O.OS]). The second highest ranked key question (information

transfer [0.17]) was slightly above the a = 0.05 threshold of significance (p = 0.068),

indicating a difference from the lowest ranked key question (sociopolitical [O.OS]).

Otherwise, there were no other discernible differences among key questions for

practicality.

Responses-Again, five Workgroup members compared responses for each key

question for practicality. Statistical tests, similar to those conducted for the key question responses with respect to importance, were performed for practicality. Results for

the Friedman and Kruskal-Wallis tests appear in table 34. Only for the most practical

key question was there evidence to indicate good agreement by Workgroup members

regarding rankings of the responses (Friedman test). For three of the key questions,

there did not appear to be significant differences among rating scores for the different

responses, as indicated by Kruskal-Wallis test probability values.

81
When we examined the Wilcoxon signed-rank test for communication (the most

practical key question), the two responses seemed to be very different, with proactively

seeking opportunities to communicate being a much more practical response than

building long-term relations (table 35). The only other key question containing any

significantly different responses appeared to be question 3, information transfer. For

this key question, there was some evidence (table 36) to suggest that free market

principles is much less practical than standardizing interfaces and exploring other

means for data management and technical transfer. No other significant differences

were apparent for responses to these two key questions.

Table P&-Probability values generated by a Wilcoxon signed-ranks test for

differences across means of the importance-rating scores for the key questions

for management concerns, applications, and decision support

Key questionsa

Info Model

Key questions* Relevance Communication transfer structures Sociopolitical

Relevance 1 .ooo

Communication .028 1 .ooo

Info transfer .028 .046 1 .ooo

Model structures .028 .173 .028 1 .ooo

Sociopolitical .753 .028 .075 .028 1 .ooo

’ See table 5 for a complete description of key questions.

Table Pg-Probability values for agreement on importance rankings (Friedman)

and differences in mean rating scores (Kruskal-Wallis) for key questions for

management concerns, applications, and decision support

Key questi0r-P Friedman test probability Kruskal-Wallis probability

Model structures 0.035 0.007

Communication .221 .041

Information transfer .119 .024

Sociopolitical .414 .068

Relevant .006 .OOl

’ See table 5 for a complete description of key questions.
Table 30-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 1 (What are the most useful model structures and outputs to support

issues in planning, operations, monitoring, and learning by resource managers,

decision makers, policy makers, and researchers?) for management concerns,

applications, and decision support

Responses to key question l8

Responses to

key question 1 a Communicate Fire regimes Data structures Framework

Communicate 1 .ooo

Fire regimes .028 1 .ooo

Data structures .249 .046 1 .ooo

Framework .463 .075 .753 1 .ooo

’ See table 5 for a complete description of responses to key question 1.

Table 31-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 3 (How can we rapidly and effectively transfer research information?)

for management concerns, applications, and decision support

ResDonses to kev auestion 3”

Responses to User Free

key question 3” Explore Improve interface support market

Explore 1 .ooo

Improve .075 1 .ooo

User interface .249 .116 1 .ooo

support .917 .075 .075 1 .ooo

Free market .345 .116 .249 .753 1 .ooo

a See table 5 for a complete description of responses to key question 3.

8 3
Table 32- Probability values generated by a Wiicoxon signed-ranks test for

differences across means for the importance-rating scores of responses to key

question 5 (How can relevant interdisciplinary resource management issues

be incorporated into models?) for management concerns, applications, and

decision support

Responses to key question 5”

Responses to key question P Involve Assign Improve

Involve 1 .ooo

Assign .028 1 .ooo

Improve .046 .028 1 .ooo

’ See table 5 for a complete description of responses to key question 5.

Table 33-Probability values generated by a Wiicoxon signed-ranks test for

differences across means for the practicality-rating scores for the key questions

for management concerns, applications, and decision support

Key questionsa

Info Model

Key questionsa Relevance Communication transfer structures Sociopolitical

Relevance 1 .ooo

Communication .043 1 .ooo

Info. transfer .686 .144 1 .ooo

Model structures .893 .225 .893 1 .ooo

Sociopolitical .138 .043 .068 .225 1 .ooo

’ See table 5 for a complete description of key questions.

Table 34-Probability values for agreement on importance rankings (Friedman)

and differences in mean rating scores (Kruskai-Waiiis) for practicality of key

questions for the Workgroup dealing with management concerns, applications,

and decision support

Key question” Friedman test probability

Model structures 0.602

Communication .025

Information transfer .256

Sociopolitical .655

Relevant .549

g See table 5 for a complete description of key questions.

Kruskal-Wallis probability

0.373

.007

.060

.745

.468

8 4
Table 35-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the practicality-rating scores for responses to key

question 2 (How do we improve communication between users and model

builders [scientists] relative to the development life cycle?) for management

concerns, applications, and decision support

Responses to key question 28 -

Responses to key question 2a Proactive Build

Proactive 1 .ooo

Build .039 1 .ooo

’ See table 5 for a complete description of responses to key question 2.

Table 36-Probability values generated by a Wilcoxon signed-ranks test for

differences across means for the practicality-rating scores for responses to key

question 3 (How can we rapidly and effectively transfer research information?)

for management concerns, applications, and decision support

Responses to key question 3a

Responses to User

key question 3’ Explore Improve interface support

Explore 1 .ooo

Improve .345 1 .ooo

User interface .500 .345 1 .ooo

support .225 .893 .345 1 .ooo

Free market .088 .893 .080 .688

* See table 5 for a complete description of responses to key question 3.

Free

market

1 .ooo

8 5
Conclusions-Useful model structures and output to support decisionmaking were

the most important issues for fire management. Improving communication between

users and model builders also appeared to be a critical issue for management, applications, and decision support. There was relatively good agreement that proactively

seeking opportunities to communicate is more important and more practical than

building long-term relations. For the development and application of fire models,

proactive communication is an issue that can be readily addressed. It also is the most

practical and cost-effective approach to ensuring that models will meet the needs of

the fire-management community. Combined high scores for importance and practicality made communication a key factor for the application of large-scale fire-disturbance

models to management and decision support.

In general, there was much less agreement by Workgroup members on the practicality

of key questions and, in particular, the practicality of responses to various key questions. This probably reflects a better understanding by the Workgroup of which things

are important to managers and policymakers and less understanding of which things

can be accomplished most practically.

8 6

r-’ I
ADDRESING FIRE-DISTURBANCE ISSUES: WORKSHOP

RESULTS AND APPLICATIONS

The structured workshop process proved to be an effective way to develop issues,

information, and approaches for addressing fire-disturbance effects on ecosystems.

Application of this process and use of the straw man document (fig. 4) differed among

workgroups, but the availability of a prescribed process and conceptual template

greatly facilitated timely discussion of topics and quantification of priorities. We

observed that resource managers in the workshop appeared to adapt to the structured

approach more readily than the scientists, a phenomenon we have observed in other

workshops and settings as well (e.g., Peterson and others 1994).

The priority research issues developed by each Workgroup tended to be quite general,

suggesting that we currently lack some of the basic information necessary to accurately assess and predict the effects of large-scale fire disturbance on natural resources. This is perhaps not surprising, because the vast majority of fire-effects

research has been conducted at small scales, making it difficult to extrapolate upward

to much larger scales (McKenzie and others 1996a). The ranked judgments of workshop participants provide a strategic approach for addressing priority research questions, with guidance for specific research approaches that will lead to timely answers

for the scientific and resource management communities.

8 7
Individual Workgroup members were internally consistent in their judgments about

priority questions and responses, although experts within a Workgroup sometimes

differed considerably in their priority ratings. The average judgments for each

Workgroup also were highly consistent. The Workgroup dealing with links among fire

effects, fuels, and climate and the Workgroup addressing fire as a large-scale disturbance had lower agreement on rankings than the other two groups. This may reflect

both the uncertainty associated with the former two topics (science questions) and the

more applied nature of the latter two topics (model development and technology

transfer).

We can infer a rather straightforward message from the highest ranked question

(What, where, and when are key factors related to fire disturbance?) and the large

amount of information generated by the Workgroup focusing on links among fire

effects, fuels, and climate: we have relatively little information about interactions

among physical and biological environmental characteristics relevant to large-scale

fire. Furthermore, we have few data on fire phenomena that can be readily applied to

large-scale fires or extrapolated from small to large scales. This leaves scientists and

manaiers with two alternatives: initiate an intensive data collection effort with emphasis

on large-scale fire, or develop or improve models that use existing data and concepts

to predict fire effects. Some mixture of these strategies would be ideal, but given that

it is unrealistic to anticipate sufficient funding for a major data collection effort, it is

more effective, at least in the short term, to improve the accuracy of existing models

that can make predictions at large spatial scales.

A related theme was discussed in great detail by the Workgroup on large-scale fire

effects, whose highest ranked question (What are the critical aspects of spatial and

temporal dynamics of fire at large scales?) emphasizes the dynamic nature of largescale fire phenomena. It was noted that there are few data available on fuels and

vegetation structure at large spatial scales, and that interactions of fuels and vegetation may be quite different at large scales than at small scales. Even if better quantitative information is available on climate-fire-vegetation interactions, it will be difficult to

predict fire occurrence and subsequent effects because of the complex and stochastic

nature of these interactions over time. Additional basic information on large-scale fire

dynamics is needed to provide the basis for more scientifically supported fire management at large spatial scales and over many decades.

Because it is unlikely that substantial quantities of new data at large spatial and

temporal scales will be collected in the near future, scientists and managers are

increasingly turning to models to assist in understanding ecosystem responses and to

predict the impacts of fire on natural resources. The highest ranked research question

for the Workgroup on fire-effects modeling (How does one validate models with respect

to error propagation?) reflects concern about problems associated with extrapolating

fire-effects data and quantitative relations from small to large scales. It was emphasized that the most useful models will be those that are spatially explicit and temporally

dynamic and where the structure of fire-effects models may differ at different spatial

8 8
and temporal scales. At the present time, it is more efficient and cost-effective to

modify and develop links for existing models rather than to build new models. Scientists and resource managers need to find ways to incorporate empirical data in models

to improve their predictive capability,

Resource managers appear ready to apply and integrate fire-effects models in their

fire-management programs, provided that those models have demonstrated good

predictive capability. The highest ranked question for the Workgroup on managerial

concerns, applications, and decision support (What are the most useful fire-effects

model structures?) indicates that resource managers are looking to scientists for

guidance on the best models for specific management applications. It is clear that

managers would like to use a hands-on approach to modeling, which allows them

to simulate the effects of various fire regimes and management options on natural

resource outputs and interactions. Effective user interfaces therefore will be critical

for successful communication and transfer of information between scientists (model

developers) and resource managers (model users).

How should the quantitative data collected on fire-effects issues at the workshop be

used in future analyses and implementation? First, one could use the results as is,

selecting those items that are most important within each category (key question or

response) and then working on the most practical of the important ones, or perhaps

developing a means of measuring combined importance and practicality. Second,

one could select specific results from each Workgroup, and use judgments from only

certain members of each Workgroup. The members whose judgments are used could

change in each case (Le., the 3-4 centroid vectors for each matrix could be used), or

the judgments from the most knowledgeable members of each Workgroup could be

followed through each analysis. A third way to treat the results is to calculate global

priorities for averaged Workgroup rankings or for each Workgroup member separately

(i.e., propagating priorities from one level [key questions] down to the next [responses]).

A final approach is to calculate true global priorities and take into account priorities of

the four primary topics. It would be appropriate for a program manager or similar

administrator to designate these high-level priorities.

We suggest limiting the number of judgments by Workgroup members that are used to

develop fire-effects research programs and priorities. Inconsistency in rankings across

Workgroup members in this effort made it difficult to obtain statistically significant

results. The intent of the workshop was to clearly state the major fire-disturbance

issues and to identify the priority tasks that lie ahead for scientists and resource

managers. It is not necessary to rely on everyone who provided judgments; other

members of the workgroups most certainly contributed in other ways (e.g., generating

discussion or providing valuable insights). Those same insightful individuals may not

necessarily be good at providing judgments or agreeing with others.

All the recent paradigms that are currently part of the managerial lexicon of the Forest

Service and other public agencies-ecosystem management, watershed analysis,

landscape design, etc.-must be addressed with concepts of large spatial and temporal

scales. The effects of fire disturbance on ecosystems are increasingly integrated into

resource management plans as a natural process, or at least a strong consideration in

8 9
fire management. The information compiled in this document represents a focused,

detailed effort to identify key issues and approaches to assess the impacts of fire

disturbance in both scientific and managerial contexts. This information can be used

as a scientific platform describing where we are and what we know, which will allow us

to better envision where it is we need to go. In so doing, it offers a template for

ongoing and future fire-effects research and for facilitating communication between

scientists and research managers. Although the total list of issues and approaches

stated here likely encompasses decades of additional research, we hope that the

highest priority questions and issues will be the ones addressed in the scientific and

resource management programs of the next few years.

ACKNOWLEDGMENTS

We appreciate the expertise and ideas of the participants in the Fire-Disturbance

Workshop (their names are shown in the chapters) who developed the concepts and

information for this paper. James Agee, Sue Ferguson, and James Saveland provided

helpful comments on an earlier draft of the manuscript. Research was supported by

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

Station.

90

r-
REFERENCES

Agee, J.K. 1990. The historical role of fire in Pacific Northwest forests. In: Walstad,

J.D.; Radosevich, S.R.; Sandberg, D.V., eds. Natural and prescribed fire in Pacific

Northwest forests. Corvallis: Oregon State University Press: 25-38.

Ages, J.K. 1993. Fire ecology of Pacific Northwest forests. Washington, DC: Island

Press. 439 p.

Albini, F.; Amin, MR.; Hungerford, R.D. [and others]. 1996. Models for fire-driven

heat and moisture transport in soils. Gen. Tech. Rep. INT-GTR-335. Ogden, UT

U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 16 p.

Albini, F.A. 1976. Estimating wildfire behavior and effects. Gen. Tech. Rep. INT-30.

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

Station. 92 p.

Albini, F.A.; Brown, J.K.; Reinhardt, E.D.; Ottmar, RD. 1995. Calibration of a large

fuel burnout model. International Journal of Wildland Fire. 5: 173-192.

Albini, F.A.; Reinhardt, E.D. 1995. Modeling ignition and burning rate of large woody

natural fuels. International Journal of Wildland Fire. 5: 81-91.

Allison, T.D.; Moeller, R.E.; Davis, M.B. 1986. Pollen in laminated sediments provides

evidence for a mid-Holocene forest pathogen outbreak. Ecology. 67: 1101-1105.

91
Anderson, H.E. 1969. Heat transfer and fire spread. Res. Pap. INT-69. Ogden, UT

U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 20 p.

Andrews, P.L. 1966. BEHAVE: fire behavior prediction and fuel modeling systemBURN subsystem, part 1. Gen. Tech. Rep. INT-194. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Research Station. 130 p.

Arens, N.C. 1990. Wildfire in the Paleozoic: preliminary results of a case study on the

fire ecology of a Pennsylvanian floodplain forest, Joggins, Nova Scotia, Canada.

In: Proceedings of an international symposium: fire and the environment: ecological

and cultural perspectives; 1990 March 20-24; Knoxville, TN. Gen. Tech. Rep. SE-69.

Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern

Research Station: 279-288.

Arno, S.F.: Reinhardt, E.D.; Scott, J.H. 1993. Forest structure and landscape patterns in the subalpine lodgepole pine type: a procedure for quantifying past and

present conditions. Gen. Tech. Rep. INT-294. Ogden, UT U.S. Department of

Agriculture, Forest Service, Intermountain Research Station. 17 p.

Arno, S.F.; Scott, J.H.; Hartwell, M.G. 1995. Age-class structure of old growth

ponderosa pine/Douglas-fir stands and its relationship to fire history. Res. Pap. INTRP-481. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 25 p.

Arno, S.F.; Simmerman, D.G; Keane, R.E. 1965. Forest succession on four habitat

types in western Montana. Gen. Tech. Rep. INT-177. Ogden, UT: U.S. Department

of Agriculture, Forest Service, Intermountain Research Station. 74 p.

Avissar, R.; Pielke, R.A. 1969. A parameterization of heterogeneous land surfaces for

atmospheric numerical models and its impact on regional modeling. Monthly

Weather Review. 117: 2113-2136.

Baker, W.L. 1969. Effect of scale and spatial heterogeneity on fire-interval distributions. Canadian Journal of Forest Research. 19: 700-706.

Baker, W.L. 1993. Spatially heterogeneous multi-scale response of landscapes to fire

suppression. Oikos. 66: 66-71.

Balling, R.C., Jr.; Meyer G.A.; Wells, S.G. 1992. Relation of surface climate and

burned area in Yellowstone National Park. Agricultural and Forest Meteorology. 60:

285293.

Barrett, S.W.; Arno, S.F.; Key, C.H. 1991. Fire regimes of western larch-lodgepole

pine forests in Glacier National Park, Montana. Canadian Journal of Forest Research. 21: 1711-1720.

Basak, I. 1990. Testing for the rank ordering of the priorities of the alternatives in

Saaty’s ratio-scale method. European Journal of Operational Research. 48: 148-l 52.

Bessie, W.C.; Johnson, E.A. 1995. The relative importance of fuels and weather on

fire behavior in subalpine forests. Ecology. 76: 747-762.

9 2
Bond, W.J.; van Wilgen, B.W. 1996. Fire and plants. London: Chapman and Hall. 263 p.

Botkin, D.B. 1993. Forest dynamics: an ecological model. New York: Oxford University

Press. 309 p.

Brenner, J. 1991. Southern Oscillation anomalies and their relationship to wildfire

activity in Florida. International Journal of Wildland Fire. 1: 73-78.

Breyfogle, S.; Ferguson, S.A. 1996. User assessment of smoke-dispersion models

for wildland biomass burning. Gen. Tech. Rep. PNW-GTR-379. Portland, OR: U.S.

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

Brown, J.K. 1961. Bulk densities of nonuniform surface fuels and their application to

fire modeling. Forest Science. 27: 667-683.

Brown, J.K.; Bevins, C.D. 1966. Surface fuel loadings and predicted fire behavior for

vegetation types in the northern Rocky Mountains. Res. Note INT-358. Ogden, UT

U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 9 p.

Brown, J.K.; Marsden, HA; Ryan, K.C; Reinhardt, E.D. 1965. Predicting duff and

woody fuel consumed by prescribed fire in the northern Rocky Mountains. Res.

Pap. INT-337. Ogden, UT: U.S. Department of Agriculture; Forest Service, Intermountain Research Station. 23 p.

Brown, J.K.; See, T.E. 1961. Downed dead woody fuel and biomass in the northern

Rocky Mountains. Gen. Tech. Rep. INT-117. Ogden, UT U.S. Department of

Agriculture, Forest Service, Intermountain Research Station. 48 p.

Byram, GM. 1959. Combustion of forest fuels. In: Davis, K.P., ed. Forest fire: control

and use. New York: McGraw-Hill: 155-l 82.

Cale, W.G. 1995. Model aggregation: ecological perspectives. In: Patten, B.C.;

Jorgensen, S.E., eds. Complex ecology: the part-whole relation in ecosystems.

Englewood Cliffs, NJ: Prentice Hall: 230-241.

Cattelino, P.J.; Noble, I.R.; Slayter, R.O.; Kessell, S.R. 1979. Predicting multiple

pathways of plant succession. Environmental Management. 3: 41-50.

Chew, J.D. [In press]. Simulating vegetative patterns and processes at landscape

scales. Ecological Applications.

Christensen, N.L., Jr. 1990. Variable fire regimes on complex landscapes: ecological

consequences, policy implications, and management strategies. In: Proceedings of

an international symposium on fire and the environment: ecological and cultural

perspectives; 1990 March 20-24; Knoxville, TN. Gen. Tech. Rep. SE-69. Atlanta,

GA: U.S. Department of Agriculture, Forest Service, Southeastern Research

Station: ix-xiii.

Clark, J.S. 1966a. Charcoal-stratigraphic analysis on petrographic thin sections:

recent fire history in northwest Minnesota. Quaternary Research. 30: 67-80.

Clark, J.S. 1966b. Effect of climate change on fire regimes in northwestern Minnesota.

Nature. 334: 233-235.

93
Clark, J.S. 199Oa. Fire and climate change during the last 750 years in northwestern

Minnesota. Ecological Monographs. 60: 135-159.

Clark, J.S. 1990b. Twentieth century climate change, fire suppression, and forest

production and decomposition in northwestern Minnesota. Canadian Journal of

Forest Research. 20: 219-232.

Clark, J.S.; Merkt, J.; Muller, H. 1989. Post-glacial fire, vegetation, and human history

of the northern Alpine forelands, southwestern Germany. Journal of Ecology. 77:

697-925.

Clark, J.S.; Royall, P.D.; Chumbley, C. 1996. The role of fire during climate change in

an eastern deciduous forest at Devil’s Bathtub, New York. Ecology. 77: 2148-2166.

Clarke, K.C.; Brass, J.A.; Riggan, P.J. 1994. A cellular automaton model of wildfire

propagation and extinction. Photogrammetric Engineering and Remote Sensing. 60:

1355-l 367

Crawford, G.; Williams, C. 1985. A note on the analysis of subjective judgment

matrices. Journal of Mathematical Psychology. 29: 387-405.

Crutzen, P.J.; Goldammer, J.G., eds. 1993. Fire in the environment: the ecological,

atmospheric, and climatic importance of vegetation fires. New York: John Wiley and

Sons. 400 p.

Daly, C.; Neilson, BP.; Phillips, D.L. 1994. A statistical-topographical model for

estimating climatological precipitation over mountainous terrain. Journal of Applied

Meteorology. 33: 140-l 58.

Davis, MA.; Botkin, D.B. 1985. Sensitivity of cool-temperate forests and their fossil

pollen record to rapid temperature change. Quaternary Research. 23: 327-340.

Deeming, J.E.; Burgen, BE.; Cohen, J.D. 1977. The National Fire-Danger Rating

System-1978. Gen. Tech. Rep. INT-39. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station.

de Jong, P. 1984. A statistical approach to Saaty’s scaling method for priorities.

Journal of Mathematical Psychology. 28: 467-478.

Elliot, J.A.; Morris, D.M.; Kantor, J.L. 1993. Studying successional pathways in

forest communities: an annotated bibliography. Info. Pap. 110. Ottawa: Ministry of

Natural Resources, Ontario Forest Research Institute. 320 p.

Fensham, R.J. 1990. Interactive effects of fire frequency and site factors in tropical

Euca/y@./s forest. Australian Journal of Ecology. 15: 255266.

Finney, M.A. 1994. Modeling the spread and behavior of prescribed natural fires. In:

Proceedings of the 12th international conference on fire and forest meteorology,

1993 October 26-28; Jekyll Island, GA. Bethesda, MD: Society of American Foresters.

Finney, M.A. 1995. FARSITE: a fire area simulator for managers. In: Weise, D.R.;

Martin, R.E., tech. coords. The Biswell symposium: fire issues and solutions in

urban interface and wildland ecosystems; 1994 Feb. 15-l 7; Walnut Creek, CA. Gen.

Tech. Rep. PSW-GTR-158. Albany, CA: U.S. Department of Agriculture, Forest

Service, Pacific Southwest Research Station: 55-56.

94
Finney, M.A.; Ryan, K.C. 1995. Use of the FARSITE fire growth model for fire prediction in U.S. national parks. In: Sullivan, J. D.; Wybo, J.L.; Buisson, L., eds., Proceedings of the international emergency management and engineering conference:

globalization of emergency management and engineering, national and international

issues concerning research and applications; 1994 May 9-12; Nice, France. San

Diego, CA: Society for Computer Simulation International: 183-l 89.

Fischer, W.; Miller, El.; Johnston, CM. [and others]. 1996. Fire effects information

system: user’s guide. Gen. Tech. Rep. INT-GTR-327. Ogden, UT U.S. Department

of Agriculture, Forest Service, Intermountain Research Station. 131 p.

Fischer, W.C. 1961. Photo guide for appraising downed woody fuels in Montana

forests: lodgepole pine, and Engelmann spruce-subalpine fire cover types. Gen.

Tech. Rep. INT-98. Ogden, UT: U.S. Department of Agriculture, Forest Service,

Intermountain Research Station. 143 p.

Fischer, WC.; Bradley, A.F. 1967. Fire ecology of western Montana forest habitat

types. Gen. Tech. Rep. INT-223. U.S. Department of Agriculture, Forest Service,

Intermountain Research Station. 95 p.

Flannigan, M.D.; van Wagner, C.E. 1991. Climate change and wildfire in Canada.

Canadian Journal of Forest Research. 21: 66-72.

Forman, R.T.T. 1995. Land mosaics: the ecology of landscapes and regions. New

York: Cambridge University Press. 832 p.

Forman, R.T.T.; Godron, M. 1986. Landscape ecology. New York: John Wiley and

Sons. 619 p.

Fosberg, M.A.; Means, L.O.; Price, C. 1993. Climate change-fire interactions at the

global scale: Predictions and limitations of methods. In: Crutzen, P.J.; Goldammer,

J.D., eds. Fire in the environment: the ecological, atmospheric, and climatic importance of vegetation fires. New York: John Wiley and Sons: 123-l 37.

Foster, DR. 1963. The history and pattern of fire in the boreal forest of southeastern

Labrador. Canadian Journal of Botany. 81: 2459-2471.

Frandsen, W.H.; Andrews, P.L. 1979. Fire behavior in nonuniform fuels. Res. Pap.

INT-232. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain

Research Station. 34 p.

Fritz, E. 1932. The role of fire in the redwood region. Circ. 323. Berkeley, CA: University of California Agricultural Experiment Station. 23 p.

Gajewski, K. 1987. Climatic changes on the vegetation of eastern North America

during the past 2000 years. Vegetatio. 68: 179-l 90.

Gardner, R.H.; Hargrove, W.W.; Turner, M.G.; Romme, W.H. 1996. Climate change,

disturbances, and landscape dynamics. In: Walker, B.; Steffan, W., eds. Global

change and terrestrial ecosystems. Cambridge, United Kingdom: Cambridge

University Press: 149-172.

95
Goldammer, J.G.; Jenkins, M.J., eds. 1990. Fire in ecosystem dynamics:

mediterranean and northern perspectives. The Hague, The Netherlands: SPB

Academic Publishing bv. 199 p.

Green, D.G. 1989. Simulated effects of fire, dispersal and spatial pattern on competition within the forest mosaics. Vegetatio. 82: 139-l 53.

Gruell, G.E. 1984. Fire and vegetative trends in the northern Rockies: interpretations

from 1871-1982 photographs. Gen. Tech. Rep. INT-158. Ogden, UT U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 117 p.

Gruell, G.E. 1985. Fire on the early western landscape: an annotated record of

wildland fires 1776-l 900. Northwest Science. 59: 97-107.

Habeck, JR. 1985. Impact of fire suppression on forest succession and fuel accumulations in long-fire-interval wilderness habitat-types. Gen. Tech. Rep. INT-182.

Ogden, UT U.S. Department of Agriculture, Forest Service, Intermountain Research

Station: 110-118.

Habeck, JR 1994. Using General Land Office records to assess forest succession in

ponderosa pine/Douglas-fir forests in western Montana. Northwest Science. 68: 69-78.

Hansen, J.; Fung, I; Lacis, A. [and others]. 1988. Global climate changes as forecast by the Goddard Institute for Space Studies three dimensional model. Journal of

Geophysical Research. 93: 9341-9364.

Hardy, C.C.; Burgan, BE.; Ottmar, R.D. [In press]. A database for spatial

assessments of fire characteristics, fuel profiles, and PM10 emissions. Journal

of Sustainable Forestry.

Harrison, H.H. 1998. A user’s guide to NFSPUFF-a dispersion model for smoke

management in complex terrain. Seattle: WYNDSOFT, Inc.; completion report. 56 p.

Available from: U.S. Department of Agriculture, Forest Service, Northern Region,

Air, Fire, and Aviation Management Staff, P.O. Box 7669, Missoula, MT 59807.

Heinselman, M.L. 1973. Fire in the virgin forests of the Boundary Waters Canoe Area,

Minnesota. Quaternary Research. 3: 329-382.

Heinselman, M.L. 1981. Fire intensity and frequency as factors in the distribution and

structure of northern ecosystems. In: Mooney, H.A.; Bonnicksen, T.M.; Christensen,

N.L. [and others], tech. coords. Proceedings of the conference on fire regimes and

ecosystem properties; 1978 December 11-15; Honolulu, HI. Gen. Tech. Rep. WO26. Washington, DC: U.S. Department of Agriculture, Forest Service: 7-58.

Heinselman, M.L. 1985. Fire regimes and management options in ecosystems with

large high-intensity fires. Gen. Tech. Rep. INT-182. Ogden, UT U.S. Department of

Agriculture, Forest Service, Intermountain Research Station.

Henderson, J.A.; Peter, D.H.; Lesher, RD.; Shaw, D.C. 1989. Forested plant

associations of the Olympic National Forest. Tech. Pap. 001-88. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Region. 502 p.

9 6
Hironaka, M. 1989. Primary successional theories. in: Ferguson, D.E.; Morgan, P.;

Johnson, F.D., eds. Proceedings-land classifications based on vegetation: applications for resource management; 1987 November 17-19; Moscow, ID. Gen. Tech.

Rep. INT-257. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station: 29-31

Hopkins, M.S.; Ash, J.; Graham, A.W. 1993. Charcoal evidence of the spatial extent

of the Eucalvptus woodland expansions and rainforest contractions in North

Queensland during the late Pleistocene. Journal of Biogeography. 20: 357-372.

Huff, M.A.; Agee, J.K. 1980. Characteristics of large lightning fires in the Olympic

Mountains, Washington. In: Martin, R.E., ed. Proceedings of the 6th conference on

fire and forest meteorology; 1980 April 22-24; Seattle. Washington, DC: Society of

American Foresters: 117-l 23.

Hungerford, RD.; Nemani, RR.; Running, S.W.; Coughlan, J.C. 1989. MTCLIM: a

mountain microclimate simulation model. Res. Pap. INT-414. Ogden, UT U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. 52 p.

Jeske, B.W.; Bevins, C.D. 1978. Spatial and temporal distribution of natural fuels in

Glacier National Park. In: Linn, R.M., ed. Proceedings of the 1 st conference on

scientific research in the national parks: 1976 November 9-13; New Orleans, LA.

Washington, DC: National Park Service: 1219-1224.

Johnson, E.A. 1979. Fire reoccurrence in the subarctic and its implications for vegetation composition. Canadian Journal of Botany. 57: 1374-l 379.

Johnson, E.A. 1992. Fire and vegetation dynamics: studies from the North American

boreal forest. New York: Cambridge University Press. 129 p.

Johnson, E.A.; Fryer, G.I.; Heathcott, M.J. 1990. The influence of man and climate

on frequency of fire in the interior wet belt forest, British Columbia. Journal of

Ecology. 78: 403-412.

Johnson, E.A.; Gutsell, S.L. 1994. Fire frequency models, methods and interpretations. Advances in Ecological Research. 25: 239-287.

Johnson, E.A.; Larsen, C.P.S. 1991. Climatically induced change in fire frequency in

the southern Canadian Rockies. Ecology. 72: 194-201.

Johnson, E.A.; van Wagner, C.E. 1985. The theory and use of two fire history

models. Canadian Journal of Forest Research. 15: 214-220.

Johnson, E.A.; Wowchuk, D.R. 1993. Wildfires in the southern Canadian Rocky

Mountains and their relationships to mid-tropospheric anomalies. Canadian Journal

of Forest Research. 23: 1213-l 222.

Keane, R.E.; Arno, S.F.; Brown, J.K. 1989. FIRESUM-an ecological process model

for fire succession in Western conifer forests. Gen. Tech. Rep. INT-266. Ogden, UT

U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 76 p.

9 7
98

r-”

Keane, R.E.; Long, D.G.; Menakis, J.P. [and others]. 1996a. Simulating coarsescale vegetation dynamics using the Columbia River basin succession modelCRBSUM. Gen. Tech. Rep. INT-GTR-340. Ogden, UT U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 50 p.

Keane, R.E.; McNicoll, C.H.; Schmidt, KM.; Garner, J.L. 1996b. Spatially explicit

ecological inventories for ecosystem management planning using gradient modeling

and remote sensing. In: Proceedings of the 6th Forest Service remote sensing

applications conference; 1996 April 29-May 3; Denver, CO. Bethesda, MD: American Society of Photogrammetry and Remote Sensing: 135-145.

Keane, R.E.; Morgan, P.; Running, S.W. 1996c. FIRE-BGC-a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of

the northern Rocky Mountains. Res. Pap. INT-RP-484. Ogden, UT: U.S. Department

of Agriculture, Forest Service, Intermountain Research Station. 122 p.

Keane, R.E.; Reinhardt, E.A.; Brown, J.K. 1994. FOFEM: a first-order fire effects

model for predicting the immediate consequences of wildland fire in the United

States. In: Proceedings of the 12th conference on fire and forest meteorology; 1993

October 26-28; Jekyll Island, GA. Bethesda, MD: Society of American Foresters:

628-631.

Keane, R.E.; Ryan, K.; Running, S.W. 1995. Simulating the effects of fire and climate

change on northern Rocky Mountain landscapes using the ecological process

model FIRE-BGC. In: Tinus, R.W., ed. Interior global change workshop. Gen. Tech,

Rep. RM-262. 39-47. Fort Collins, CO: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station.

Keetch, J.J.; Byram, G.M. 1988. A drought index for forest fire control. Res. Pap.

SE-38. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern

Research Station. 32 p.

Kercher, J.R.; Axelrod, M.C. 1984. A process model of fire ecology and succession in

a mixed conifer forest. Ecology. 65: 1725-l 742.

Kessell, S.R. 1979. Gradient modeling: resource and fire management. New York:

Springer Verlag. 432 p.

Kessell, S.R.; Fischer, W.C. 1981. Predicting postfire plant succession for fire management planning. Gen. Tech. Rep. INT-94. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 19 p.

King, A.W.; Johnson, A.R.; O’Neill, R.V. 1991. Transmutation and functional representation of heterogeneous landscapes. Landscape Ecology. 5: 239-253.

Knight, D.H. 1987. Parasites, lightning and the vegetation mosaic in wilderness

landscapes. In: Turner, M.G., ed. Landscape heterogeneity and disturbance. New

York: Springer Verlag: 59-83.

Levin, S.A. 1992. The problem of pattern and scale in ecology. Ecology. 73: 1943-l 967.
Lotan, J.E.; Brown, J.K.; Neuenschwander, I.F. 1985. Role of fire in lodgepole pine

forests. In: Baumgartner, D.M.; Krebill, G.; Amott, J.T; Weetman, G.F., eds. Proceedings of the symposium on lodgepole pine: the species and its management;

1984 May 14-16; Spokane, WA. Pullman, WA: Cooperative Extension, Washington

State University: 133-l 52.

Loveland, T.R.; Merchant, J.M.; Ohlen, D.O.; Brown, J.F. 1991. Development of a

land-cover characteristics database for the conterminous U.S. Photogrammetric

Engineering and Remote Sensing. 57: 1453-1463.

Marsden, MA. 1983. Modeling the effect of wildfire frequency on forest structure and

succession in the northern Rocky Mountains. Journal of Environmental Management. 16:45-62.

Masters, A.M. 1990. Changes in forest fire frequency in Kootenay National Park,

Canadian Rockies. Canadian Journal of Botany. 68: 1763-1767.

McKenzie, D.M.; Peterson, D.L.; Alvarado, E. 1998a. Extrapolation problems in

modeling fire effects at large spatial scales: a review. International Journal of

Wildland Fire. 6: 165-176.

McKenzie, D.M.; Peterson, D.L.; Alvarado, E. 1998b. Predicting the effects of fire on

large-scale vegetation patterns in North America. Res. Pap. PNW-RP-489. Portland

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

Station. 38 p.

Minnich, R.A. 1983. Fire mosaics in southern California and northern Baja California.

Science. 219: 1287-l 294.

Neilson, R.P.; King, G.A. 1992. Continental-scale biotic response to climatic change.

In: McKenzie, D.H.; Hyatt, D.E.; McDonald, V.J., eds. Ecological indicators, volume

2. London: Elsevier Applied Science: 1015-l 040.

Neilson, RR.; King, G.A.; Lenihan, J. 1994. Modeling forest response to climatic

change: the potential for large emissions of carbon from dying forests. In: Kanninen,

M., ed. Proceedings of an IPCC AFOWS workshop on carbon balance of the world’s

forested ecosystems: towards a global assessment; 1992 May 11-l 5; Joensuu,

Finland. Helsinki: Publications of the Academy of Finland.

Noble, I.R.; Slatyer, R.O. 1977. Postfire succession of plants in mediterranean

ecosystems. In: Mooney, H. A.; Conrad, C.E., eds. Proceedings of the symposium

on environmental consequences of fire and fuel management in mediterranean

climate ecosystems; 1977 August l-5; Palo Alto, CA. Gen. Tech. Rep. WO-3.

Washington DC: U.S. Department of Agriculture, Forest Service: 27-36.

Ottmar, R.D.; Burns, M.F.; Hall, J.N.; Hanson, A.D. 1992. CONSUME users guide.

Gen. Tech. Rep. PNW-GTR-304. Portland, OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station. 118 p.

9 9
Ottmar, RD.; Sandberg, D.V. 1985. Calculating moisture content of 1 OOO-hr timelag

fuels in western Oregon and western Washington. Res. Pap. PNW-336. Portland,

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

Station. 16 p.

Overpeck, J.T.; Rind, D.; Goldberg, R. 1990. Climate-induced changes in forest

disturbance and vegetation. Nature. 343: 51-53.

Payette, S.; Morneau, C.; Sirois, L.; Desponts, M. 1989. Recent fire history of the

northern Quebec biomes. Ecology. 70: 656-673.

Paysen, T.E.; Narog, M.G.; Cohen, J.D. 1998. The science of prescribed fire: to

enable a different kind of control. In: Pruden, T.L.; Brennan, L.A., eds. Fire in

ecosystem management: shifting the paradigm from suppression to prescription.

Tallahassee, FL: Tall Timbers Research Station: 31-36.

Peterson, D.L.; Ryan. K.C. 1986. Modeling postfire conifer mortality for long range

planning. Environmental Management. 10: 797-808.

Peterson, D.L.; Schmoldt, D.L.; Eilers, J.M. [and others]. 1993. Guidelines for

evaluating air pollution impacts on class I wilderness areas in California. Gen. Tech.

Rep. PSW-GTR-136. Albany, CA: U.S. Department of Agriculture, Forest Service,

Pacific Southwest Research Station. 34 p.

Peterson, D.L.; Schmoldt, D.L.; Silsbee, D.G. 1994. A case study of resource

management planning with multiple objectives and projects. Environmental Management. 18: 729-742.

Peterson, J.; Schmoldt, D.L.; Peterson, D.L. [and others]. 1992. Guidelines for

evaluating air pollution impacts on class I wilderness areas in the Pacific Northwest.

Gen. Tech. Rep. PNW-GTR-299. Portland OR: U.S. Department of Agriculture,

Forest Service, Pacific Northwest Research Station. 83 p.

Pickett, S.T.A.; White, P.S., eds. 1985. The ecology of natural disturbance and patch

dynamics. San Diego: Academic Press. 472 p.

Pickford, S.G.; Fahnestock, G.R.; Ottmar, R. 1980. Weather, fuel, and lightning fires

in Olympic National Park. Northwest Science. 54: 92-105.

Prentice, l.c.; Monserud, R.A.; Smith, TM.; Emanuel, W.R. 1993. Modeling largescale vegetation dynamics. In: Solomon, A.M.; Shugart, H.H., eds., Vegetation

dynamics and global change. London: Chapman and Hall: 235-250.

Price, C.; Rind, D. 1992. A simple lightning parameterization for calculating global

lightning distributions. Journal of Geophysical Research. 97: 9919-9933.

Price, C.; Rind, D. 1994. The impact of 2 x CO, climate on lightning-caused fires.

Journal of Climate. 7: 1484-1494.

Pyne, S.J. 1982. Fire in America: a cultural history of wildland and rural fire. Princeton,

NJ: Princeton University Press. 654 p.

Pyne, S.J. 1984. Introduction to wildland fire: fire management in the United States.

New York: John Wiley and Sons. 455 p.

100
Quigley, TM.; Haynes, R.W.; Graham, R.T., tech. eds. 1996. Integrated scientific

assessment for ecosystem management of the interior Columbia basin. Gen. Tech.

Rep. PNW-GTR-382. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station.

Rastetter, E.B.; King, A.W.; Cosby, B.J. [and others]. 1992. Aggregating fine-scale

ecological knowledge to model coarser-scale attributes of ecosystems. Ecological

Applications. 2: 55-70.

Reed, W.J. 1994. Estimating the historic probability of stand-replacement fire using

age-class distribution of undisturbed forest. Forest Science. 40: 104-l 19.

Reinhardt, E.; Keane, R.E.; Brown, J.K. 1996. First order fire effects model-FOFEM

4.~user’s guide. Gen. Tech. Rep. INT-GTR-344. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain Research Station. 65 p.

Renkin, R.A.; Despain, D.G. 1992. Fuel moisture, forest type, and lightning-caused

fire in Yellowstone National Park. Canadian Journal of Forest Research. 22: 37-45.

Rogers, P. 1996. Disturbance ecology and forest management: a review of the

literature. Gen. Tech. Rep. INT-GTR-336. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 16 pi

Romme, W.H.; Despain, D.G. 1969. Historical perspective on the Yellowstone fires of

1988. Bioscience. 39: 695-699.

Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland

fuels. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Research Station. 40 p.

Rothermel, R.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 Research Station. 46 p.

Ryan, K.C. 1991. Vegetation and wildland fire: implications of global climate change.

Environment International. 17: 169-178.

Ryan, K.C.; Reinhardt, E.D. 1986. Predicting postfire mortality of seven western

conifers. Canadian Journal of Forest Research. 18: 1291-l 297.

Saaty, T.L. 1960. The analytic hierarchy process. New York: McGraw-Hill.

Saaty, T.L. 1990. Multicriteria decision making: the analytic hierarchy process. Pittsburgh: RWS Publications.

Sandberg, D.V.; Peterson, J. 1984. A source strength model for prescribed fires in

coniferous logging slash. In: Proceedings of the annual meeting of the Air Pollution

Control Association; 12-14 November 1984; Portland, OR. Pittsburgh: Air Pollution

Control Association.

Schiff, A. 1962. Fire and water; scientific heresy in the Forest Service. Cambridge,

MA: Harvard University Press.

Schlesinger, M.E.; Mitchell, J.P.B. 1987. Climate model simulations of the equilibrium

climatic response to increased carbon dioxide. Reviews of Geophysics. 25: 760-798.

101
102

Schmoldt, D.L. 1992. Bringing technology to the resource manager...and not the

reverse. In: Proceedings of the ASPRS/ACSM/RT 1992 convention: monitoring and

mapping global change, volume 5; 1992 August 3-8; Washington, DC. Bethesda,

MD: American Society for Photogrammetry and Remote Sensing, American Congress on Surveying and Mapping: 62-74.

Schmoldt, D.L.; Peterson, D.L. 1991. Applying knowledge-based methods to the

design and implementation of an air quality workshop. Environmental Management.

15: 623-634.

Schmoldt, D. L.; Peterson, D.L. 1997. Using the AHP in a workshop setting to elicit

and prioritize fireresearch needs. In: Proceedings of the ACSM/ASPRS/RT 1997

convention, volume 4, resource technology; 7-10 April 1997; Seattle. Bethesda MD:

American Society of Photogrammetry and Remote Sensing: 151-l 62.

Schmoldt, D.L.; Peterson, D.L. [In press]. Analytical group decision making in

natural resources: methodology and application. Forest Science.

Shands, W.E.; Hoffman, J.S., eds. 1987. The greenhouse effect, climate change, and

U.S. forests. Washington, DC: The Conservation Foundation. 304 p.

Show, S.B.; Kotok, E.I. 1923. Forest fires in California 1911-l 920-an analytical

study. Circ. 243. Washington, DC: U.S. Department of Agriculture. 80 p.

Show, S.B.; Kotok, E.I. 1924. The role of fire in the California pine forests. Bull. 1294.

Washington, DC: U.S. Department of Agriculture. 80 p.

Shugart, H.H.; West, DC. 1980. Forest succession models. Bioscience. 30: 308-313.

Sierra Nevada Ecosystem [SNEP]. 1996. Status of the Sierra Nevada: final report to

Congress. Davis, CA: Center for Water and Wildland Resources, University of

California. 22 p.

Slmard, A.J. 1991. Fire severity, changing scales, and how things hang together.

International Journal of Wildland Fire. 1: 23-34.

Singh, G.; Kershaw, A.P.; Clark, R. 1961. Quaternary vegetation and fire history in

Australia. In: Gill, A.M.: Groves, R.H.; Noble, I.R., eds. Fire and the Australian biota.

Canberra: Australian Academy of Science: 23-54.

Smith, R.L.; Bush, R.J.; Schmoldt, D.L. 1995. A hierarchical analysis of bridge

decision makers. Wood and Fiber Science. 27: 225-238.

Spies, T.A.; Franklin, J.F.; Thomas, T.B. 1988. Coarse woody debris in Douglas-fir

forests of western Oregon and Washington. Ecology. 69: 1689-l 702.

Steele, R.; Geier-Hayes, K. 1989. The Douglas-fir/ninebark habitat type in central

Idaho: succession and management. Gen. Tech. Rep. INT-252. Ogden, UT U.S.

Department of Agriculture, Forest Service, Intermountain Research Station. 65 p.

Stickney, P.F. 1985. Data base for early postfire succession on the Sundance Burn,

northern Idaho. Gen. Tech. Rep. INT-189. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 121 p,
Strauss, D.; Bednar, L.; Mees, R. 1989. Do one percent of forest fires cause ninetynine percent of the damage? Forest Science. 35: 319-328.

Swain, A.M. 1973. A history of fire and vegetation in northeastern Minnesota as

recorded in lake sediment. Quaternary Research. 3: 383-396.

Swanson, F.J.; Franklin, J.F.; Sedell, JR. 1990. Landscape patterns, disturbance,

and management in the Pacific Northwest, USA. In: Zonneveld, I. S.; Forman,

R.T.T., eds. Changing landscapes: an ecological perspective. New York: SpringerVerlag: 191-213.

Swetnam, T.W.; Baisan, C.H. 1998. Historical fire regime patterns in the southwestern

United States since AD 1700. In: Allen, C.D., tech. ed. Fire effects in Southwestern

forests, proceedings of the 2d La Mesa fire symposium; 1994 March 29-31; Los

Alamos, NM. Gen. Tech. Rep. RM-GTR-286. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Research Station: 11-32.

Swetnam, T.W.; Betancourt, J.L. 1990. Fire-Southern Oscillation relations in the

Southwestern United States. Science. 249: 961-1076.

SYSTAT. 1992. Statistics, version 5.2 edition. Evanston, IL: SYSTAT Inc.

Thornton, P.E.; Running, S.W.; White, MA. 1997. Generating surfaces of daily

meteorological variables over large regions of complex terrain. Journal of Hydrology.

190: 214-251.

Turner, M.G. 1989. Landscape ecology: the effect of pattern on process. Annual

Review of Ecology and Systematics. 20: 171-l 97.

Turner, M.G.; Gardner, R.H., eds. 1991. Quantitative methods in landscape ecology.

New York: Springer-Verlag. 536 p.

Turner, M.G.; Gardner, R.H.; Dale, V.H.; O’Neill, R.V. 1989. Predicting the spread of

disturbance across heterogeneous landscapes. Oikos. 55: 121-l 29.

Turner, M.G.; Romme, W.H. 1994. Landscape dynamics in crown fire ecosystems.

Landscape Ecology. 9: 59-77.

van Wagner, C.E. 1978. Age-class distribution and the forest fire cycle. Canadian

Journal of Forest Research. 6: 220-227.

van Wagner, C.E. 1987. Development and structure of the Canadian forest fire

weather index system. For. Tech. Rep. 35. Chalk River, Ontario: Canadian Forestry

Service. 37 p.

van Wagtendonk, J. W. 1972. Fire and fuel relationships in mixed conifer ecosystems

of Yosemite National Park. Berkeley: University of California. 121 p. Ph.D. dissertation.

Vargas, G.L. 1982. Reciprocal matrices with random coefficients. Mathematical

Modeling. 3: 69-61.

Vasquer, A.; Moreno, J.M. 1993. Sensitivity of fire occurrence to meteorological

variables in Mediterranean and Atlantic areas of Spain. Landscape and Urban

Planning. 24: 129-l 42.

103
VEMAP members. 1995. Vegetation/ecosystem modeling and analysis project:

comparing biogeography and biogeochemistry models in a continental-scale study

of terrestrial ecosystem responses to climate change and CO, doubling. Global

Biogeochemical Cycles. 9: 407-437.

Wein, R.W.; MacLean, D.A., eds. 1983. The role of fire in northern circumpolar

ecosystems. New York: John Wiley and Sons. 322 p.

Wells, C.G.; Campbell, R.E.; Debano, L.F. [and others]. 1979. Effects of fire on soil:

a state-of-knowledge review. Gen. Tech. Rep. WO-7. Washington, DC: U.S. Department of Agriculture, Forest Service. 34 p.

Williams, J.T.; Rothermel, R.C. 1992. Fire dynamics in northern Rocky Mountain

stand types. Res. Note INT-405. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Forest Service, Intermountain Research Station. 4 p.

Wright, H.A.; Bailey, A.W. 1982. Fire ecology: United States and southern Canada.

New York: John Wiley and Sons. 501 p.

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

model. Gen. Tech. Rep. INT-133. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Intermountain Research Station. 112 p.

Yarie, J. 1981. Forest fire cycles and life tables: a case study from interior Alaska.

Canadian Journal of Forest Research. 11: 554-562.

Zahedi, F. 1986. A simulation study of estimation methods in the analytic hierarchy

process. Socio-Economic Planning Science. 20: 347-354.

104


Schmoldt, Daniel L.; Peterson, David L.; Keane, Robert E.; Lenihan, James

M.; McKenzie, Donald; We/se, David R.; Sandberg, David V. 1999. Assessing the effects of fire disturbance on ecosystems: a scientific agenda for

research and management. Gen. Tech. Rep. PNW-GTR-455. Portland, OR:

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

Station. 104 p.

A team of fire scientists and resource managers compiled scientific recommendations for future activities on (1) links among fire effects, fuels, and climate; (2) fire

as a large-scale disturbance; (3) fire-effects modeling structures; and (4) managerial concerns, applications, and decision support. Although we clearly need more

large-scale fire-effects data, it would be better to improve and link existing models

that simulate fire effects in a georeferenced format while integrating empirical data

as they become available. This effort should focus on improved communication

between modelers and managers and on predicting the interactions of fire and

potential climatic change at very large spatial scales.

Keywords: Analytic hierarchy process, ecological disturbance, fire effects, largescale fire, modeling.

The Forest Service of the U.S. Department of

Agriculture is dedicated to the principle of multiple

use management of the Nation’s forest resources for

sustained yields of wood, water, forage, wildlife, and

recreation. Through forestry research, cooperation with

the States and private forest owners, and management

of the National Forests and National Grasslands,

it strives-as directed by Congress-to provide

increasingly greater service to a growing Nation.

The U.S. Department of Agriculture (USDA) prohibits

discrimination in all its programs and activities on the

basis of race, color, national origin, gender, religion,

age, disability, political beliefs, sexual orientation, or

marital or family status. (Not all prohibited bases apply

to all programs.) Persons with disabilities who requi’&

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 df discrimination, write USDA,

Director, Office of Civil Rights, Room 326-W, Whitten

Building, 14th and Independence Avenue, SW, Washington,

DC 20250-9410 or call (202) 720-5964 (voice and TDD).

USDA is an equal opportunity provider and employer.

Pacific Northwest Research Station

333 S.W. First Avenue

P.O. Box 3890

Portland, Oregon 97208-3890