1

Assessing the Expected Effects of Wildfire on Vegetation 

Condition on the Bridger-Teton National Forest, Wyoming, USA

Joe H. Scott, Donald J. Helmbrecht, and Matthew P. Thompson

Scott, J. H.; Helmbrecht, D. J.; Thompson, M. P. 2014. Assessing the expected 

effects of wildfire on vegetation condition on the Bridger-Teton National 

Forest, Wyoming, USA. Res. Note: RMRS-RN-71. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 

36 p.

Abstract

Characterizing wildfire risk to a fire-adapted ecosystem presents particular challenges due to its broad spatial extent, inherent complexity, and the 

difficulty in defining wildfire-induced losses and benefits. Our approach couples stochastic wildfire simulation with a vegetation condition assessment 

framework to estimate the conditional and expected response of vegetation condition to wildfire. We illustrate application of this framework for the 

Bridger-Teton National Forest (BTNF) in western Wyoming, USA. Results 

illustrate generally positive net effects of wildfire on vegetation condition 

across the major forested biophysical settings on the Forest, supporting the 

notion that wildfire can play a role in restoring or enhancing the ecological 

integrity of landscapes affected by fire exclusion. These results carry significant implications for future management of wildfire on the BTNF, and 

highlight temporal relationships between short-term incident response and 

long-term ecological integrity.

Keywords: departure analysis; fire effects; LANDFIRE; ecological integrity

Forest Rocky Mountain Research Note

Service Research Station RMRS-RN-71 November 2014

United States Department of Agriculture
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Introduction

An understanding of wildfire risk—the likelihood and magnitude of 

wildfire-induced loss or benefit to highly valued resources and assets 

(HVRAs)—is fundamental to effective wildland fire management. 

Risk-based information supports planning efforts aimed at minimizing wildfire-related losses and maximizing wildfire-related benefits. 

The primary modeling components of wildfire risk assessment include 

exposure analysis and effects analysis (Thompson and Calkin 2011). 

Assessment of HVRA exposure to wildfire—the likelihood and intensity of wildfire where an HVRA exists—is increasingly used to inform 

fire management planning and fuel treatment prioritization across the 

wildland fire management spectrum, including pre-wildfire planning 

(Scott and others 2012a,b), fuel treatment design (Ager and others 

2010), and wildfire incident response (Calkin and others 2011b). 

Further, assessing the potential effects of fire on HVRAs under varying 

levels of exposure can provide a more comprehensive basis for evaluating the potential consequences of wildfire (Thompson and others 

2013a).

Although the term “risk” is typically associated with the notion of 

loss—indeed, most efforts to estimate wildfire risk limit their focus to 

adverse fire effects—wildfires can also result in substantial ecological 

benefits. A comprehensive assessment of wildfire risk should therefore consider the beneficial ecological effects of wildfire (Thompson 

and Calkin 2011; Thompson and others 2013a). This recognition is 

consistent with Federal wildland fire policy that (1) establishes risk 

management as the basis for wildland fire management, and (2) allows 

for flexibility when responding to incidents to account for beneficial 

effects (National Interagency Fire Center 2009). Furthermore, accounting for ecological considerations in wildland fire management 

planning is consistent with the USDA Forest Service 2012 planning 

rule’s emphasis on the restoration and maintenance of resilient ecosystems (Federal Register 77 No. 68) as well as the management policies 

of many national parks and protected areas. Managers of Federal lands 

face the difficult task of protecting resources and assets susceptible to 

wildfire-induced loss while simultaneously allowing fire to play its natural role in sustaining ecosystems.

Many recent wildfire risk assessment efforts have had direct ties to resource objectives and the beneficial role of fire, for instance, evaluating 

dry forest restoration and impacts to fire-dependent wildlife habitat. 
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However, the consideration of broader ecological objectives regarding 

ecosystem condition has been limited. While early national-scale efforts did include fire-adapted ecosystems as an HVRA (Calkin and 

others 2010; Thompson and others 2011a,b), the analyses used only 

coarse scale information on fire regime group and did not integrate 

spatial information related to the extent, complexity, and/or variability 

of ecosystem components. A more refined, and more intensive, approach could instead consider fire-adapted ecosystems on the basis 

of variables like biophysical setting and the geographic distribution 

of successional states. Our primary objective for this paper is to describe how we addressed the challenges of assessing risk to fire-adapted 

ecosystems by integrating vegetation condition and wildfire risk assessment frameworks. Further, we illustrate a landscape-scale application 

of this integrated framework to assess the expected effects of wildfire on 

vegetation condition, and discuss how assessment results can inform 

wildland fire management decisions.

The biological elements (e.g., vegetation, wildlife) and ecological processes (e.g., nutrient cycling, soil development, hydrologic function) 

of fire-adapted ecosystems have evolved with recurring wildfire, and 

wildfire plays a critical role in their sustainability (Keane and others 

2002). Wildfire is a driving force behind the composition, structure, 

and function of fire-adapted ecosystems (Agee 1998). However, the 

fire regime characteristics—primarily the frequency, severity, and size 

of fires—vary widely among individual ecosystems (Agee 1998; Noss 

and others 2006). Thus, an assessment of the effects of wildfire on fireadapted ecosystems begins with classification of individual ecosystem 

types based on the characteristic fire regime of the system. Vegetation 

classifications based on biophysical site characteristics (e.g., soil, climate, topography) and disturbance regimes are readily available for 

delineating broad ecosystem types across a landscape (e.g., Rollins 

2009).

Spatial and temporal variability in fire regime characteristics also exist 

within individual ecosystem types due to heterogeneity in fuel, temporal fuel dynamics, weather, topography, and ignitions (Ehle and Baker 

2003; Noss and others 2006; Turner and Romme 1994). For example, 

an ecosystem associated with a “low severity” fire regime may occasionally experience moderate- and high-severity fires in particular locations 

and under particular conditions. This variability leads to a dynamic 

mix of ecosystem states, within any individual ecosystem type, characterized by compositional, structural, and functional components. The 

spatial characterization of fire-adapted ecosystems, therefore, should 

account for the interdependence and dynamic nature of disturbance 

and vegetation on the condition of the system as a whole.
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An assessment of current vegetation condition—current proportional 

distribution of successional states relative to a reference distribution—

serves as a measure of ecosystem resiliency and ecological integrity. The 

USDA Forest Service 2012 planning rule directives define ecological 

integrity as “the quality or condition of an ecosystem when its dominant 

ecological characteristics... occur within the natural range of variation and can withstand and recover from most perturbations imposed 

by natural environmental dynamics or human influence” (Proposed 

FSH 1909.12 Chapter 10.5). The Interagency Fire Regime Condition 

Class (FRCC) Guidebook standard landscape mapping methodology 

(Barrett and others 2010) provides a foundation for such a vegetation 

condition assessment (VCA). Our primary assumptions in using this 

VCA approach are: (1) vegetation composition and structure are dominant characteristics of fire-adapted ecosystems and serve as indicators 

for other functional characteristics, and (2) historical conditions provide an acceptable reference for assessing current vegetation condition 

and ecological integrity based on the concept that the conditions with 

which biologic and other ecosystem components have evolved are likely to sustain them in the future (Landres and others 1999; Keane and 

others 2009; Swetnam and others 1999).

Wildfire Simulation

Spatial wildfire simulation is the backbone of quantitative risk assessment (Finney 2005; Miller and Ager 2012; Scott and others 2013b). 

FSim, the large-fire simulator (Finney and others 2011), is a comprehensive, stochastic wildfire occurrence, growth, and suppression 

simulation system that pairs a wildfire growth model (Finney 1998, 

2002) and a model of ignition probability with simulated weather 

streams in order to simulate wildfire ignition and growth for tens of 

thousands of fire seasons. The results of these simulations are used to 

estimate, in raster format, the annual burn probability (BP) and conditional flame-length probability (FLPi

) across the landscape. FSim BP

is the annual probability of burning; it is estimated by dividing the 

number of simulated fire seasons that burned each pixel by the total 

number of simulated fire seasons. FLPi is the conditional probability of 

wildfire burning in the i

th flame-length category, given that a wildfire 

occurs at all. At a given pixel, FLPi values across the range of flamelength categories sum to one. FSim results have been used for spatial 

risk analyses in a number of contexts (Calkin and others 2010; Scott 

and others 2012a,b; Thompson and others 2011, 2013a,b).
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Simulation of daily values of Energy Release Component (ERC) of the 

National Fire Danger Rating System is the foundation of FSim’s operation. ERC is calculated from historical weather data (Cohen and 

Deeming 1985). The simulated ERC is used in two ways: first, to determine the probability of a wildfire start for each day, and second, to 

determine which of three fuel moisture scenarios to use for the day. 

The three scenarios correspond to ERC classes with breaks at the 80th, 

90th, and 97th percentile ERC values. ERC is simulated for each day 

of each simulated fire season based on the historical seasonal trend in 

mean and standard deviation of ERC using temporal autocorrelation 

(Finney and others 2011). Wildfire growth occurs only on days for 

which the simulated ERC exceeds the 80th percentile. Simulated wildfire growth for each day of each fire is also a function of wind speed 

and direction. Wind characteristics for each day are determined by a 

random draw from the historical monthly joint frequency distribution 

of wind speed and direction. This draw is independent of ERC, and 

each day’s draw is independent of the others.

A wildfire in FSim grows until it is either contained or self-extinguishes. FSim includes a suppression module based on a containment 

probability model (Finney and others 2009) that relates the likelihood of wildfire containment on a given day to current and previous 

fire growth. Containment success is simulated stochastically based on 

comparison of a random draw with the modeled containment success 

probability. Self-extinguishment occurs when ERC remains below the 

80th percentile value for several days in a row, if the entire perimeter 

reaches non-burnable land cover, or if the simulation reaches the last 

calendar day of the year.

Vegetation Condition Assessment

The VCA methodology uses biophysical settings (BpSs) as the primary environmental descriptor (Barrett and others 2010). BpSs 

are mapped using characteristics of the biophysical environment 

and named and described based on approximation of the historical 

disturbance regime, thus providing a suitable characterization of a 

fire-adapted ecosystem. Multiple successional states may compose 

an individual BpS at any one time. The LANDFIRE data (Rollins 

2009) used in this case study apply the classification developed by 

Hann (2003) that is also applied in the FRCC Guidebook (Barrett 

and others 2010). This classification allows up to five natural successional states, referred to as S-Classes and labeled A through E, 

to be represented in an individual BpS. Additional S-Classes identify 

current vegetation conditions that are uncharacteristic of the reference condition. These include native vegetation with composition or 
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structure components outside the range of variation estimated for the 

reference period (uncharacteristic native; UN) and introduced exotic 

vegetation (uncharacteristic exotic; UE). The majority of S-Classes for 

each BpS are mapped based on rules describing specific thresholds for 

lifeform, cover, and height data as documented in BpS model descriptions (LANDFIRE 2007). In some cases, the cover and height rules are 

specific to an existing vegetation type or group of types.

For each BpS, the VCA methodology compares the current amount 

of mapped S-Classes for each BpS within a stratification, such as a 

watershed, to a modeled reference distribution. The reference amount 

represents the mean proportion of each stage’s class as estimated by simulation of historical disturbance probabilities and vegetative succession 

in the Vegetation Dynamics Development Tool (ESSA Technologies 

Ltd. 2007) over multiple simulations.

A stratification, or assessment unit, must be identified for each BpS 

prior to conducting the VCA. An assessment unit is the contiguous 

land area within which the current distribution of S-Classes is compared to the reference distribution. The appropriate assessment unit 

size for a BpS is related to its dominant disturbance regime (Barrett 

and others 2010). If the assessment unit is too small relative to the historical size of disturbances, a single disturbance event, even one within 

the natural range of variation, could result in one successional stage 

dominating the landscape, indicating departure from the reference 

condition. Conversely, if the assessment unit is too large, then even an 

abnormally large disturbance event may not change the current distribution of S-Classes, failing to show a departure from the reference 

condition.

For each assessment unit where a BpS occurs, S-Class departure from 

the reference condition is quantified as the S-Class percent difference 

(Barrett and others 2010)

SSSSSSSSSSSS pppppppppppppp dddddddddddddddddddd = CCCC − RRRR

mmaaaa RRRR, CCCC ∗ 100 (1)

where CP is the current proportion and RP is the reference proportion 

for the S-Class. Positive values of this measure indicate that the current 

proportion of the S-Class exceeds the reference proportion; negative 

values indicate that the current is less than the reference proportion. If 

the current proportion is twice the reference proportion, the S-Class 

percent difference is +50 percent; if the current proportion is half of 

the reference, then percent difference is -50 percent.
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Risk Assessment Framework

A spatially explicit, quantitative wildfire risk assessment framework 

has been recommended for use to inform a variety of land and fire 

management decisions across a range of planning scales (Calkin and 

others 2011a). The fundamental components of this risk framework 

are wildfire likelihood, intensity, and effects (Finney 2005; Miller and 

Ager 2012; Scott 2006; Scott and others 2013b). The term “effects 

analysis” is often used to describe a comprehensive risk assessment that 

addresses all three fundamental components. The three primary data 

requirements to assess the expected response of HVRAs to wildfire include: (1) geospatial data of burn probability and wildfire intensity 

generated from a stochastic wildfire simulation, (2) geospatially identified HVRAs, and (3) response functions that describe the effects of fire 

on each HVRA across a range of fire intensity levels. Typically, the response functions describe fire effects as the net change in value (NVC) 

of the HVRA should it experience a fire of a given intensity. A response function value of –100 indicates the greatest possible reduction 

of value, or loss, whereas +100 indicates the greatest possible increase 

in value, or benefit. Pairing components 1 and 2 alone provides important information regarding where on the landscape HVRAs will likely 

interact with wildfire of different intensity levels (also known as exposure analysis). The addition of component 3 further characterizes the 

effects on HVRAs of this interaction with fire, which can lead to very 

different prioritization strategies than those based on exposure alone 

(Thompson and others 2013a).

The risk framework quantifies risk, for each pixel on a landscape, as 

the expected value of net value change, which is often simply called 

expected net value change, written as E(NVC). This calculation combines an estimate of NVC for a given fire intensity level (NVCi

) with 

FLPi. Calculating E(NVC) is a two step process. First, the conditional 

NVC, or C(NVC), is calculated as the sum-product of FLPi and NVCi

over a range of flame length classes

C(NNNNNN) = FFFFFF! ∗ NNNNNN! (2)

where C(NVC) is the conditional response of the HVRA to wildfire, 

given that one occurs. The expected value of NVC is

E(NNNNNN) = C(NNNNNN) ∗ BBBB (3)
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Because the term “risk” is often associated solely with adverse wildfire 

effects, we will hereafter use the terms “expected response” and “expected effects” in place of “risk,” and “effects analysis” in place of “risk 

assessment” when using this framework to quantify wildfire effects on 

vegetation condition. Expected response can be positive or negative.

Where empirical data and integrated modeling tools are lacking, structured protocols for eliciting and encapsulating expert judgment are 

available to characterize broad scale fire effects to HVRAs. In fact, a 

cornerstone of our overall modeling approach was reliance on local 

knowledge and expertise. We thereby outline a risk-based approach to 

quantitatively assess expected wildfire effects to the ecological integrity 

of fire-adapted ecosystems. Our intent is to demonstrate application of 

a novel approach to quantifying expected fire response in an ecological 

context, considering the amount of S-Classes across a BpS within an 

assessment unit, as a measure of the ecological integrity of fire-adapted 

ecosystems.

Methods

Our overall analytical approach was to first quantify the current 

vegetation condition—the current proportion of area represented 

by individual S-Classes relative to a reference proportion—for each 

biophysical setting, and then to apply a standard quantitative risk assessment framework based upon its results. This approach couples an 

assessment of vegetation condition with a stochastic wildfire simulation and a risk assessment framework to estimate the conditional and 

expected response of vegetation condition to wildfire.

Study Area

The case study we present originated from a broader assessment of 

wildfire risk to HVRAs within Grand Teton National Park and the 

Bridger-Teton National Forest (BTNF) in western Wyoming, USA 

(Scott and others 2013a). In that assessment, each agency first identified a suite of HVRAs to be analyzed, and then characterized the 

susceptibility and relative importance of those HVRAs according to 

the assessment process outlined by Thompson and others (2013a). In 

this paper, we specifically focus only on the quantification of the effects 

of wildfire on vegetation condition within the BTNF, using VCA as a 

foundation.
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A wide range of biophysical settings and vegetation types occur within 

the 3.5 million acre BTNF (figure 1). The valley-bottoms, at roughly 2,000 m elevation, are covered by grasslands and grass mixed with 

sagebrush (Artemisia tridentata). The highest peaks in the study area 

exceed 3,600 m; the terrain above 3,000 m typically does not support 

wildfire spread due to the prevalence of rock and persistent snow. The 

slopes between the valley-bottoms and the peaks are covered by coniferous forests, montane meadows, and stands dominated by quaking 

aspen (Populous tremuloides).

Wildfire Simulation

A prerequisite to spatial wildfire simulation is the development of a fire 

modeling landscape file (LCP)—a suite of geospatial data layers characterizing vegetation, fuel, and topography. The vegetation and fuel 

layers for the LCP were derived from geospatial data describing BpS 

and existing vegetation characteristics. We used LANDFIRE version 

1.0.5 (also referred to as “Refresh 2001”) of these data (Rollins 2009) 

for an extent that includes a minimum buffer of 10 miles around the 

BTNF. This buffer allows for the simulation of wildfire spread onto the 

study area from adjacent land without introducing an artificial edge effect. To generate the most up-to-date and locally relevant LCP possible 

we consulted with local resource specialists to (1) critique and update 

vegetation characteristics and surface fire behavior fuel model mapping 

rules, and (2) to get updates for recent disturbances not accounted for 

in the LANDFIRE version 1.0.5 data. We acquired geospatial data on 

wildfire severity for the time period of 2000 through 2010 from the 

Teton-Interagency fire ecologist, and geospatial data on overstory canopy loss due to insects and disease for the time period of 2000 through 

2008 from the USDA Forest Service Remote Sensing Applications 

Center (Goetz and others 2009). Surface fire behavior fuel mapping 

rules were adjusted based on local resource specialist expertise such that 

simulated fire behavior qualitatively agreed with expected fire behavior.

We used FSim to quantify overall BP and FLP across six fire intensity 

levels (FILs) at each point across the landscape. FSim uses the standard 

classification of FIL developed for the Fire Program Analysis project 

(table 1). Fire intensity in FSim incorporates the effects of relative 

spread direction (heading, flanking, and backing) as well as variability 

in wind speed, wind direction, and fuel moisture. An unpublished report on file at the BTNF (Scott and others 2013a) provides additional 

detail on our wildfire simulation modeling, including our analysis of 

local wildfire and weather history for input into FSim.
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Figure 1. Spatial distribution of the five primary LANDFIRE biophysical settings assessed on the Bridger-Teton 

National Forest and surrounding area. 
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Table 1—Flame length range associated 

with six Fire Intensity Levels (FILs) as 

used in the FSim large-fire simulator.

Fire Intensity Flame length

Level (FIL) range (feet)

FIL1 0-2

FIL2 2-4

FIL3 4-6

FIL4 6-8

FIL5 8-12

FIL6 12+

We supplied FSim with weather data for the period of 1990-2010 from 

the Raspberry Remote Automated Weather Station (RAWS), located 

centrally in the landscape. We gathered fire occurrence data (start location and date, cause, and final size) for all jurisdictions in the analysis 

area during the period of 1990-2009, and critiqued the data to identify 

and remove duplicate and erroneous values. We used FireFamilyPlus 

software to generate logistic regression coefficients for estimating the 

probability of fire occurrence as a function of ERC. Because multiple 

fires can start on the same day, we also provided a table to FSim indicating the historical distribution of the number of fires per fire-day. 

FSim uses these historical fire occurrence parameters to simulate the 

ignition of wildfires as a function of simulated ERC.

FSim can optionally use information regarding the spatial density of 

ignitions across the landscape. We used an ignition density grid that 

was generated for a companion study on the same landscape (Scott 

and others 2012a) using a statistical modeling approach similar to that 

of Syphard and others (2008). Using FSim, we simulated 20,000 fireseason iterations at a 90-m raster resolution (2 ac per pixel).

Vegetation Condition Assessment

We used the Interagency Fire Regime Condition Class (FRCC) 

Guidebook standard landscape mapping methodology (Hann and others 2008) to determine, for each BpS, the percent difference of each 

S-Class relative to the reference condition (Equation 1). The same 

LANDFIRE version 1.0.5 BpS and existing vegetation data layers used 

for creating the LCP were also used to generate an S-Class data layer. 

There are 29 LANDFIRE BpS classes (Rollins 2009) mapped within 

the BTNF (excluding the bare ground, open water, and perennial ice 

and snow classes). We ultimately identified five BpSs that represent the 

predominant fire-adapted forest ecosystems of the BTNF, accounting 

for 71 percent of the land area (figure 1).
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Resource specialists critiqued the LANDFIRE S-Class mapping rules 

for errors and local applicability. Adjustments were made to fix issues 

with rule overlap and local applicability of uncharacteristic native conditions. The adjusted rules were applied using GIS software. Next, 

resource specialists developed S-Class transition rules in order to update the ca. 2001 geospatial data for wildfire and insect and disease 

disturbance not accounted for in the LANDFIRE version 1.0.5 data as 

discussed above.

Per guidance from the Interagency FRCC Guidebook (Barrett and 

others 2010), we delineated assessment units based on the dominant 

historical fire regime of each BpS. We ultimately delineated two landscape levels for the assessment (figure 2). The larger level uses major 

rivers and hydrologic unit boundaries to delineate major mountain 

ranges of the BTNF. This level is applied to the three BpSs associated 

with fire regime groups IV and V (table 2). Sub-basins (that is, 4th level 

hydrologic unit code) within the larger landscapes represent the second 

landscape level, which we applied to the two BpSs associated with fire 

regime group III.

We used version 2.2.0 of the FRCC Mapping Tool to conduct the assessment. The tool calculates the S-Class percent difference (Equation 1) 

for each combination of BpS, S-Class and assessment unit. We then 

classified the S-Class percent difference values into three categories representing the status of the S-Class: deficit, similar, or surplus (table 3). 

This metric is especially informative because it indicates whether a particular S-Class needs to be reduced, maintained, or recruited to move 

a BpS towards the reference condition within a particular assessment 

unit.

Effects Analysis

The effects analysis integrates the VCA and wildfire simulation results (figure 3). To characterize the effects of wildfire on VCA we held 

a workshop at which resource specialists from the BTNF defined a 

response function for each unique combination of BpS, S-Class and 

status present within the BTNF. Response functions quantify net value 

change, expressed as a relative percentage loss or benefit, as a function of fire intensity and other landscape characteristics (Thompson 

and others 2011, 2013a). A positive value in a response function indicates a benefit of wildfire at that intensity level; a negative response 

function value indicates a loss, or decrease in value. Response function 

values may range from –100 (greatest possible loss of resource value) to 

+100 (greatest increase in value). In the design and implementation of 

the workshop we adhered to guidance for expert judgment elicitation 

(Kohl and others 2010; Kuhnert and others 2010).
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Figure 2. Assessment unit delineations for the mountain range landscape level (four assessment units; 

used for the two biophysical settings in Fire Regime Group IV and V) and the sub-basin landscape 

level (12 assessment units; used for the four biophysical settings in Fire Regime Group III (table 2)). 
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Table 3—Thresholds used by the FRCC Mapping Tool ver. 

2.2.0 to calculate S-Class status.

Absolute difference between current

S-Class proportion and reference

S-Class Status proportiona

Deficit -100% and < -33%

Similar ≥ -33% and ≤ 33%

Surplus >33% and 100% 

a This assessment was conducted using the Interagency FRCC 

Guidebook version 1.3.0 (Hann and others 2008) methodology 

and version 2.2.0 of the FRCC Mapping Tool. Version 3.0 of the 

Guidebook (Barrett and others 2010) and version 3.1.0 of the 

FRCC Mapping Tool use different thresholds.

Table 2—Landscape level and associated fire regime group for each biophysical setting (BpS) assessed on the Bridger-Teton National 

Forest. Assessment units are delineated in Figure 2. 

Landscape 

level

Number of 

assessment 

units

Fire regime Associated biophysical settings (BpSs)

Group Frequency Severity

Complete name and 

model number Short name

Sub-Basin 12 III 35 – 200 

years

Low – Mixed

Northern Rocky Mountain 

Subalpine Woodland and 

Parkland - 2110460

Subalpine woodland

Middle Rocky Mountain 

Montane Douglas-fir Forest 

and Woodland - 2111660

Douglas-fir

Mountain 

Range 4

IV 35 – 200 

years Replacement Rocky Mountain Aspen Forest and Woodland - 2010110 Aspen

V 200+ years Replacement - Any

Rocky Mountain Subalpine 

Dry-Mesic Spruce-Fir Forest 

and Woodland - 2110550

Dry spruce-fir

Rocky Mountain Subalpine 

Mesic-Wet Spruce-Fir Forest 

and Woodland - 2110560

Wet spruce-fir
15

Figure 3. Calculating conditional and expected response of vegetation condition to wildfire 

is a function of three modeling processes: vegetation condition assessment, exposure and 

effects analysis, and wildfire simulation.
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In developing a response function for a combination of BpS, S-Class 

and status, we asked the resource specialists to consider how a wildfire occurring at each of six different fire intensity levels would affect 

the vegetation type, cover, and height, and therefore S-Class. Then we 

asked them to consider how that change in S-Class would affect the 

overall proportion of S-Classes within each BpS. That is, we asked the 

specialists to consider not only direct wildfire impacts to S-Class, but 

also how those impacts would lead to desirable or undesirable shifts 

in S-Class proportion at the stratum level (i.e., BpS). For instance, a 

wildfire-related transition from an S-Class with a surplus of acres to 

an S-Class with a deficit of acres can be considered ecologically beneficial—wildfire is expected to move the proportion of both S-Classes 

towards the reference proportion thus improving the ecological integrity of the stratum as a whole.

For each pixel on the landscape, we used a GIS to first calculate the 

conditional response to wildfire (CR) as

CCCC = FFFFFF! ∗ RRRR! (4)

Conditional response is an estimate of the likely response, given that a 

fire occurs. Next, we calculated the expected value of the response to 

wildfire (ER) as

EEEE = CCCC ∗ BBBB (5)

Expected response incorporates the likelihood of occurrence. Finally, 

we computed the mean values of BP, CR and ER for the unique combinations of BpS, S-Class and status. By coupling the land area associated 

with each of those combinations with the mean BP, we also estimated 

the expected annual area burned.

Results

Wildfire Simulation

The wildfire simulation resulted in an annual mean of 5.9 fires exceeding 250 ac, with a mean size of 6,108 ac. This corresponds to a 

mean annual area burned of 36,508 ac/yr compared to the historical 

20-year mean of 30,001 ac/yr. More importantly, FSim estimates how 

those acres burned are distributed across the landscape as burn probability (figure 4). The spatial pattern of BP is a function of ignition 

density and spread rate potential within an area of the landscape. 
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Figure 4. Simulated annual burn probability across the fire modeling landscape. Areas in brown have the highest burn 

probabilities; areas in blue the lowest. 
18

The south-central portion of the landscape has both low ignition 

potential and low spread rate potential (fuel is predominantly sparse 

grass), so that portion has some of the lowest burn probabilities. The 

highest BP values occur in the moderate grass and grass-shrub fuelbeds 

(fuel models GR2, GR4 and GS2; Scott and Burgan 2005) because 

these fuel models exhibit relatively high spread rates.

The simulation of fire intensity in FSim inherently incorporates the effects of relative spread direction (heading, flanking, and backing) and 

variability in wind speed, wind direction, and fuel moisture. These factors, coupled with a heterogeneous mix of fuel and topography, result 

in a spatially variable distribution of burn probability both within and 

among FILs (figure 5). Conditional probabilities are generally lower 

for greater fire intensities, with very low probabilities for FIL 6 (flame 

length >12 feet). Some of the highest conditional burn probabilities are 

for FIL 2, which corresponds to 2- to 4-foot flame lengths.

Vegetation Condition Assessment

Each of the five BpSs assessed has one or more S-Classes in deficit 

or surplus status, suggesting varying but widespread departure of the 

current vegetation condition from the reference condition (table 4). A 

combination of BpS and S-Class showing more than one status indicates a variation in status among assessment units. Small amounts of 

S-Class U were found in several BpSs, primarily due to the mapping 

of riparian existing vegetation types (EVTs) to the non-riparian BpSs. 

This discrepancy results from differences in the LANDFIRE BpS and 

EVT mapping methodologies. BpS is “coarser” in concept than EVT 

because it is mapped using biophysical gradient modeling, without the 

integration of remotely sensed imagery that is used for mapping EVT 

(Rollins 2009). Therefore, EVT data may reflect subtleties gleaned 

from the remotely sensed vegetation that are missed by the BpS mapping process, possibly resulting in an invalid characterization of the 

UN S-Class where EVT and BpS seem to conflict. Because this potential problem does not cover a significant area, we have chosen to 

exclude results for S-Class U from the analysis.

In all four mountain range assessment units, the dry spruce-fir BpS 

(covering 37 percent of the BTNF) showed a deficit of the middevelopment S-Class (B) and a surplus of early-development and 

spruce-fir dominated late-development S-Classes (A and D). The status of lodgepole-dominated late-development S-Class C was similar 

to the reference condition (table 4). For the subalpine woodland BpS 

(11 percent), there is a deficit of mid-development/closed-structure 

S-Class B and a surplus of late-development/open-structure S-Class D. 
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Figure 5. Conditional flame-length probability (FLP) by fire intensity level (FIL). These probability values 

are conditional on wildfire occurrence and are thus quantified on the scale of 0-1. For burnable pixels, 

the sum of FLP values across all six FILs is exactly 1. Fire intensity levels are classified by simulated 

flame length. FIL1 = 0-2 ft; FIL2 = 2-4 ft; FIL3 = 4-6 ft; FIL4 = 6-8 ft; FIL5 = 8-12 ft; FIL6 = 12+ ft.
20

Table 4. S-Class status results and reference proportion.

Biophysical 

setting

S-Class: 

Development 

stage/structure

Status

Reference 

Deficit Similar Surplus proportion (%)

Dry sprucefir

A: Early/All X 5

B: Mid/All X 30

C: Late/Alla X 50

D: Late/Allb X 15

Subalpine 

woodland

A: Early/All X X 25

B: Mid/Closed X 35

C: Mid/Open X X 10

D: Late/Open X 5

E: Late/Closed X X 25

Wet sprucefir

A: Early/All X X 10

B: Mid/Closedc X 20

C: Mid/Open X 10

D: Late/Open X 40

E: Late/Closed X 20

Aspen

A: Early/All X X 15

B: Mid/All X 20

C: Late/Closedd X X 25

D: Late/Open X 25

E: Late/Closede X X 15

Douglas-fir 

A: Early/All X X 10

B: Mid/Closed X 10

C: Mid/Open X X 10

D: Late/Open X 50

E: Late/Closed X X 20

a Lodgepole dominant.

b Spruce-fir dominant.

c S-Class B is in such a deficit that it does not exist on the current landscape.

d Aspen dominant.

e Conifers dominant or co-dominant.
21

The early-development, mid-development/open structure, and latedevelopment/closed-structure S-Classes (A, C and E) each fall into 

two status classes due to variation across assessment units. In the wet 

spruce-fir BpS (9 percent), the status of the early-development S-Class 

(A) was split between similar and surplus; the remaining S-Classes were 

either in deficit or surplus status, but not similar. In the aspen BpS (9 

percent), the status of each S-Class is deficit or surplus in at least one 

assessment unit, indicating widespread departure from the reference 

condition. However, the status was similar for at least some assessment 

units for the early development (A), aspen-dominated late-development/closed-structure (C), and conifer-dominated late-development/

closed-structure (E) S-Classes. In the Douglas-fir BpS (5 percent), the 

mid-development/closed-structure S-Class (B) is in deficit in all assessment units, but the status of the other S-Classes was similar to the 

reference condition in at least one all assessment units.

Effects Analysis

The response functions produced by the workshop participants indicate a range of both positive and negative effects of fire (table 5). Many 

of the response functions have strong positive values, reaching as high 

as +100 percent where late-development/open-structure in the aspen 

BpS (S-Class D) is in surplus, indicating an expectation for substantial 

ecological benefits due to wildfire. Such benefits were largely assigned 

where a given S-Class is in surplus, in some cases even for very high fire 

intensities, especially if fire is expected to transition the surplus S-Class 

to an S-Class currently in deficit. Benefits are also associated with 

maintenance of an S-Class that is already in similar or deficit status, 

though these benefits were generally not as great. Lastly, the distribution of fire-related benefits and losses varies according to S-Class status 

and FIL, indicating the added value of assessing vegetation departure 

at the S-Class level, rather than merely indicating whether a given BpS 

is departed.

Differences in mean annual burn probability among BpSs are driven 

by ignition density and spread rate potential within and adjacent to the 

BpS. Wildfire simulation results show a wide range in BpS exposure 

to wildfire, with the aspen and Douglas-fir BpSs roughly three times 

as likely to experience wildfire as the subalpine woodland (figure 6). 

The mean conditional response for all BpS classes is positive, indicating that, on average, wildfire is expected to have a net beneficial effect 

on vegetation condition, moving the current distribution of successional stages towards the reference distribution in each of the BpSs 

(figure 6). Mean conditional response integrates information from tabular 

response functions and conditional flame-length probabilities across 

FILs, summarized across all combinations of BpS, S-Class, and status. 
22

Table 5. Response functions for each combination of biophysical setting (BpS), S-Class and status present within the BridgerTeton National Forest. The response function value indicates the relative percentage change in value after burning at the 

given fire intensity level (FIL) (measured in flame length classes). Highlighted cells indicate a non-negative response to 

wildfire. This table only presents combinations that actually exist on the landscape, and is not an exhaustive listing of 

all possible combinations of BpS, S-Class and status.

Biophysical setting

S-Class: 

Development 

stage/structure

Status FIL1 FIL2 FIL3 FIL4 FIL5 FIL6

Dry spruce-fir

A: Early/All Surplus 40 40 -20 -30 -30 -30

B: Mid/All Deficit 30 15 -10 -50 -70 -80

C: Late/All Similar 30 30 15 0 -60 -70

D: Late/All Surplus 10 20 40 40 20 10

Subalpine woodland

A: Early/All surplus 30 30 -10 -20 -20 -20

A: Early/All similar 30 30 -10 -20 -20 -20

B: Mid/closed Deficit 30 40 0 -70 -80 -80

C: Mid/Open Similar 20 30 0 -60 -70 -70

C: Mid/Open deficit 30 40 0 -70 -80 -80

D: Late/Open Surplus 10 20 40 60 20 0

E: Late/Closed similar 30 20 0 -60 -90 -90

E: Late/Closed deficit 40 30 -10 -70 -100 -100

Wet spruce-fir

A: Early/All Surplus 30 30 -20 -30 -30 -30

A: Early/All similar 30 30 -20 -30 -30 -30

C: Mid/Open deficit 30 40 0 -70 -80 -80

D: Late/Open Surplus 10 20 40 60 20 0

E: Late/Closed deficit 40 30 -10 -70 -100 -100

Aspen

A: Early/All similar 0 0 -10 -30 -50 -70

A: Early/All Deficit 0 0 -20 -40 -60 -80

B: Mid/All deficit -10 -10 -20 -40 -60 -80

C: Late/Closed similar 20 20 0 -20 -40 -60

C: Late/Closed Deficit 10 10 -10 -30 -50 -70

D: Late/Open Surplus 10 20 40 100 100 100

E: Late/Closed similar 10 10 10 10 10 10

E: Late/Closed deficit 0 0 0 0 0 0

Douglas-fir 

A: Early/All Surplus 30 30 -20 -30 -30 -30

A: Early/All similar 30 30 -20 -30 -30 -30

B: Mid/closed Deficit 40 40 20 -50 -50 -50

C: Mid/Open Similar 50 60 60 0 -50 -50

C: Mid/Open Deficit 60 70 70 0 -60 -60

D: Late/Open similar 50 60 60 20 -40 -50

E: Late/Closed surplus 0 20 70 70 -40 -50

E: Late/Closed similar 70 70 70 70 -40 -50
23

Figure 6. Mean annual burn probability and mean conditional response to wildfire for 

each of five biophysical settings assessed on the Bridger-Teton National Forest. Light 

gray reference lines indicate equal expected response.

The strongest positive response is in the Douglas-fir BpS. The product 

of annual burn probability and conditional response yields expected 

response. Thus, although they vary in both burn probability and conditional response, the overall expected responses for the dry spruce-fir, 

wet spruce fir, and aspen BpSs are roughly equal (figure 6).

Variation in BP and CR exists among S-Classes of a BpS (figure 7). 

Particularly noteworthy is the range of conditional responses to 

wildfire, especially for the aspen BpS, which shows a slightly negative 

response to wildfire where the early-development S-Class is similar or 

in deficit to the reference proportion and where the mid-development 

S-Class (B) is in deficit. By cross-referencing these results with the response functions, the causes of the negative response become apparent—the 

response function indicates strong adverse effects of fire in the middle and 

higher FILs for all three of those combinations of S-Class and status. 
24

Figure 7. Mean annual burn probability and mean conditional response to wildfire of succession 

class (S-Class) and status combinations for each of five biophysical settings on the Bridger-Teton 

National Forest. Light gray reference lines indicate equal expected response (see table 6 for 

details).
25

The land area of each BpS and S-Class status combination multiplied 

by the mean annual burn probability yields an expectation for annual 

area burned (table 6). The dry spruce-fir BpS has the highest expected 

annual area burned, due in part to its large land area. Expected response 

is presented as a percentage of the maximum expected response occurring in any combination of BpS, S-Class and status. In this analysis, 

the maximum expected response occurs where the mid-development/

open-structure S-Class of the Douglas-fir BpS (C) is in deficit. This 

S-Class also has the second-highest conditional response (behind 

where the late-development/closed-structure S-Class (E) is similar to 

the reference proportion, which exhibits the third-highest expected response. In other words, taken as a whole, the Douglas-fir BpS exhibits 

the most beneficial effects of wildfire, both conditional and expected. 

Within the Douglas-fir BpS, all combinations of S-Class and status 

show a positive mean response to wildfire. The greatest conditional 

benefits occur where the late-development/closed-structure S-Class (E) 

is similar to the reference proportion. Interestingly, in assessment units 

where that S-Class is in surplus is among the three S-Classes with the 

least wildfire benefit. The reason for this divergence of conditional response within a single S-Class can be seen in the response functions. 

Where this S-Class is similar to the reference condition, the response 

function values are strongly positive for low-intensity fire, indicating 

the benefit of low-intensity fire in maintaining the S-Class through 

under-burning. The response functions reflect that low-intensity fire 

is less beneficial where late-development/closed-structure is in surplus. 

The aspen BpS is the only BpS where an S-Class exhibits a negative 

mean conditional response. The mean conditional response of the early 

and mid-development aspen S-Classes are all slightly negative, regardless of status.

Even though most S-Classes exhibit a positive mean condition response, individual grid cells within an S-Class can still have a negative 

conditional response (figure 8) if response functions include negative 

values. In the Aspen BpS, for which some S-Classes had a negative 

mean conditional response, 14 percent of land area exhibits a negative 

response. In the Douglas-fir BpS, which had the most positive conditional response, only 2 percent of the BpS had a negative net response, 

owing to negative response function values at higher FILs.
26

Table 6. Summary of land area, mean burn probability, expected area burned and mean expected and conditional response to 

wildfire of five BpSs on the Bridger-Teton National Forest. Mean expected response (per pixel) is expressed as a percentage 

of the maximum response. Mean conditional response is expressed on the response-function scale (-100 to +100). A BpS 

and S-Class combination can have more than one status if vegetation condition varies among assessment landscapes.

Mean response per pixel

Biophysical 

setting

S-Class: 

Development 

stage/status Status

Land area 

(ac)

Mean 

annual burn 

probability 

(fraction)

Expected 

annual area 

burned (ac)

Expected 

response 

(percent of 

max)

Conditional 

response

Dry spruce-fir

A: Early/All Surplus 253,779 0.002864 726 26 21.6

B: Mid/All Deficit 18,973 0.002042 40 10 11.8

C: Late/All Similar 497,850 0.002023 1,006 22 23.6

D: Late/All Surplus 492,179 0.002127 1,048 15 17.1

Subalpine 

woodland

A: Early/All Surplus 30,839 0.001684 52 12 16.7

A: Early/All Similar 94,873 0.001259 119 11 21.0

B: Mid/closed Deficit 381 0.001074 0 15 32.2

C: Mid/Open Similar 1,712 0.001384 2 13 22.8

C: Mid/Open Deficit 14,802 0.001124 17 14 30.4

D: Late/Open Surplus 204,215 0.001287 262 9 16.6

E: Late/Closed Similar 3,773 0.001406 5 15 25.3

E: Late/Closed Deficit 35,240 0.001293 44 18 32.5

Wet spruce-fir

A: Early/All Surplus 34,096 0.003630 124 25 16.4

A: Early/All Similar 12,916 0.003837 49 22 13.5

C: Mid/Open Deficit 12,276 0.003121 40 37 28.3

D: Late/Open Surplus 229,435 0.002678 615 24 20.9

E: Late/Closed Deficit 27,636 0.002722 74 21 18.3

Aspen

A: Early/All Similar 37,355 0.005669 213 -15 -6.3

A: Early/All Deficit 2,459 0.003877 10 -13 -8.0

B: Mid/All Deficit 16,109 0.003323 54 -18 -12.8

C: Late/Closed Similar 4,460 0.002487 10 19 18.0

C: Late/Closed Deficit 33,325 0.002838 94 9 7.8

D: Late/Open Surplus 204,922 0.003402 697 32 22.2

E: Late/Closed Similar 20,917 0.003505 74 15 10.0

E: Late/Closed Deficit 5,733 0.003648 20 0 0.0

Douglas-fir

A: Early/All Surplus 11,649 0.003960 47 27 16.1

A: Early/All Similar 11,673 0.004379 52 25 13.7

B: Mid/closed Deficit 15 0.003550 0 51 34.2

C: Mid/Open Similar 2,773 0.002865 7 65 53.9

C: Mid/Open Deficit 6,133 0.003684 22 100 64.4

D: Late/Open Similar 87,663 0.003318 292 77 54.7

E: Late/Closed Surplus 24,777 0.004155 104 26 14.8

E: Late/Closed Similar 28,652 0.002521 72 73 69.2
27

Figure 8. Histograms of the conditional response to wildfire of individual succession classes (S-Classes) 

within five biophysical settings (BpSs) on the Bridger-Teton National Forest. Stacked bars reflect that 

different S-Classes can produce the same effect, depending on their response functions and exposure to 

fire intensity. Likewise, a particular combination of BpS and S-Class can be found in multiple conditional 

response bars, indicating variability in fire intensity or S-Class status across the landscape (see table 6 for 

S-Class descriptions within each BpS). 
28

In addition to the tabular and graphical summaries reported above, the 

effects analysis produces a map of conditional and expected response of 

vegetation condition to wildfire (figure 9). As also seen in figure 8, very 

little land area resulted in negative conditional or expected response. A 

small amount of the landscape exhibited a neutral response, bracketing zero. The majority of the landscape exhibits varying degrees of net 

benefit. Expected response is the product of condition response and 

BP, so the small differences in the two maps are due to variation in BP

across the landscape.

Figure 9. Conditional response (left) and expected response (right) of vegetation condition to wildfire. Expected response is the product 

of conditional response and annual burn probability (figure 4). The X-axes of the histograms correspond to the classes indicated in the 

legends. Neutral response is depicted in gray.
29

Discussion

Our results illustrate generally positive net effects of wildfire on vegetation condition across the major forested BpSs on the BTNF, supporting 

the notion that wildfire can play a role in restoring or enhancing the 

ecological integrity of landscapes affected by fire exclusion. Our results 

further support the assertions that incorporating information regarding expected response to wildfire can be much more informative than 

analysis of wildfire characteristics in and of themselves. Partitioning 

response functions according to indicators of vegetation condition, or 

ecological integrity, such as S-Class status, can lead to risk-informed 

restoration decision processes. These results carry significant implications for future management of wildfire on the Forest, and highlight 

temporal relationships between short-term incident response and longterm ecological integrity. Granted, our results provide but a snapshot 

of a dynamic system, but the analyses demonstrated here can be periodically performed over time to monitor trends in ecological integrity 

and associated wildfire risks.

At least three opportunities stem directly from this integrated assessment of vegetation condition and wildfire effects. First, restoration 

needs could be prioritized on the basis of S-Class status. That is, areas 

of high vegetation condition departure could be targeted for intentional vegetation manipulation to move towards desired conditions. 

Second, prescribed fire planning could build off analysis of restoration 

needs, targeting specific fire intensities in specific locations to achieve 

restoration objectives. These planning efforts could tie directly to expert-defined response functions, which neatly encapsulate information 

required regarding desired intensities. Third, land management units 

could seek to refine their land and fire management plans to account 

for the spatial distribution of wildfire effects on vegetation condition 

across the landscape. For instance, in areas of expected net benefit, 

wildfire response objectives could be spatially delineated to promote 

the management of fire for resource benefits (Thompson and others 

2013c). This type of analysis would dovetail nicely with a trend of 

increased use of geospatial information and risk assessment results to 

support wildfire incident response planning.
30

Any comprehensive analysis of wildfire risk will need to consider the full 

suite of HVRAs, including socioeconomic HVRAs. Whereas our use 

of the VCA approach and focus on an ecological HVRA allowed beneficial effects of wildfire to be shown, in many contexts and locations it 

will be difficult to promote wildland fire (both planned and unplanned 

ignitions) as a management or restoration tool due to high potential 

for loss. In the broader Teton Interagency Risk Assessment (Scott and 

others 2013a) we found wildfire threats were highest primarily in locations near highly important and highly susceptible HVRAs, including 

the wildland urban interface, high-value BTNF investments, and critical wildlife habitat. These threats tended to be spatially concentrated. 

That is, the majority of the risk on a landscape may be housed in a relatively small spatial extent. Explicit evaluation of potential fire-related 

benefits may engender support for additional use of wildland fire as a 

management tool, or enable less aggressive suppression where it does 

not directly threaten resources and assets more susceptible to loss.

Another caveat relates to our use of multiple models to analyze vegetation departure (VCA), to simulate wildfire (FSim), and to characterize 

fire effects (response functions), and the associated potential for errors and uncertainties. A particular concern relates to the potential for 

under-prediction of crown fire behavior, which could affect estimates 

of flame lengths and patterns of burn probability in forested areas. 

However, the 20-year historical mean annual area burned corresponded 

well with the simulated mean annual area burned, suggesting reasonable validity of the burn probability results. We’d further note that we 

undertook great effort to work with local experts to calibrate vegetation and fuel input layers and to examine simulation assumptions and 

results. Reliance on local practitioner and resource specialist judgment 

was a common thread throughout our entire analytical process.

In terms of recommendations for future analyses, a critical component 

is up-front investment to set the stage for success. Both the vegetation 

condition and wildfire effects assessments require intensive geospatial data management and expertise in natural resources management 

and modeling. Appropriate resources and expertise should be identified early in the process. Following the principles of expert judgment 

elicitation, it is recommended to distribute information to resource 

specialists prior to holding the response function workshops, and to 

invest time in clearly articulating assessment objectives. This early engagement is important not only to familiarize experts with the process 

and to start thinking though potential fire effects, but also to help 

identify additional geospatial information to incorporate into response 

functions for characterizing fire effects. Time is always a constraint in 

such efforts, but where possible, scheduling a pre-workshop to discuss 

and critique conceptual models of post-fire S-Class transitions could 

be helpful for defining response functions.
31

FSim was designed to be used by specialized analysts to assess national fire program management alternatives (Finney and others 2011). 

Although it has subsequently been used a number of times at finer 

scales, FSim remains a system in continued development and is available to a relatively small set of users. Fortunately, alternatives exist. 

FlamMap5—the most-recent version of the FlamMap software (Finney 

2006)—has the ability to generate the gridded burn probability and 

flame-length probability information needed for this assessment method. Unlike FSim, the results from FlamMap5 do not correspond to 

a year or fire season. Instead, FlamMap5 results relate to a relatively 

short duration “problem-fire” scenario—a specified wind speed, wind 

direction, fire duration, and set of fuel moisture contents. The primary 

stochastic element of FlamMap5 is ignition location. Depending on 

how the user defines the problem-fire scenario, the relative frequency of 

different flame-length classes could be quite different from FSim. FSim 

simulates fire intensity in all weather scenarios, whereas FlamMap5 

is used to simulate problematic scenarios. Because of this difference, 

FlamMap5 is likely to generate higher conditional flame lengths than 

FSim; that, in turn, affects conditional wildfire response. Nonetheless, 

the use of FlamMap5 could be a useful first step in the assessment of 

the expected effects of wildfire on vegetation condition.

Graphically depicting the results as the combination of BP and conditional net response enables exploration of implications for fire 

management (figure 10). While stylized, this framework provides a 

useful model for thinking through the implications of HVRA exposure 

and effects and how various management strategies may target different 

risk factors. Wildfire risk mitigation activities (fire prevention, preparedness, suppression, hazard reduction and susceptibility reduction) 

are indicated anywhere conditional wildfire response is negative. Areas 

of the landscape where expected response is most negative (high BP

and strongly negative conditional response) may be explored as higher 

priority treatment areas relative to areas where conditional response is 

less negative and BP is low. Treatments in such areas could be strategically placed to lower BP values across the landscape and/or to reduce 

localized fire intensities to reduce CR. Where conditional response is 

positive, a variety of wildland fire management practices are available. 

Where both CR and BP are high, managing wildfires (unplanned ignitions) may provide the greatest chance of success. Prescribed fire may 

be considered where the conditional effects of wildfire are positive, but 

not likely to occur without management intervention. Prescribed fire 

may also be effective even where CR is negative if there is some intensity level that is achievable with prescribed fire for which the response 

function value is positive.
32

Acknowledgments

We would like to thank Martha Williamson, Teton Interagency Fire 

Planner, as well as staff and personnel from the Bridger-Teton National 

Forest and Grand Teton National Park. The National Fire Decision 

Support Center and the Rocky Mountain Research Station supported 

this work. We thank Carol Miller and Wendel Hann for their valuable 

reviews of this manuscript.

Figure 10. Implications of conditional response and burn probability on wildfire risk mitigation priorities 

and wildland fire management. Where conditional response is positive and burn probability (BP) is 

high, management of wildfire for these benefits is a good strategy. Where conditional response is high 

but BP is low, prescribed fire can be used to capture the benefits of wildland fire.
33

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set for use with Rothermel’s surface fire spread model. General Technical Report. 

RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 72 p.

Scott, J. H.; Helmbrecht, D. J.; Parks, S. A.; Miller, C. 2012a. Quantifying the threat 

of unsuppressed wildfires reaching the adjacent wildland-urban interface on the 

Bridger-Teton National Forest, Wyoming. Fire Ecology. 8(2): 125-142.

Scott, J. H.; Helmbrecht, D. J; Thompson, M. P.; Calkin, D. E. 2012b. Probabilistic 

assessment of wildfire hazard and municipal watershed exposure. Natural Hazards. 

64(1): 707-728.

Scott, J. H.; Helmbrecht, D. J.; Williamson, M. 2013a. Response of highly valued resources and assets to wildfire within the Bridger-Teton National Forest and 

Grand Teton National Park. Unpublished report. On file at: U.S. Department 

of Agriculture, Forest Service, Bridger-Teton National Forest Supervisor’s Office, 

Jackson, WY. Online: http://pyrologix.com/wp-content/uploads/2014/04/

ScottHelmbrechtWilliamson_2013.pdf.

Scott, J. H.; Thompson, M. P.; Calkin, D. E. 2013b. A wildfire risk assessment 

framework for land and resource management. General Technical Report. RMRSGTR-315. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 83 p.

Swetnam, T. W.; Allen, C. D.; Betancourt, J. L. 1999. Applied historical ecology: 

using the past to manage for the future. Ecological Applications. 9(4): 1189-1206.

Thompson, M. P.; Calkin, D. E. 2011. Uncertainty and risk in wildland fire management: A review. Journal of Environmental Management. 92: 1895-1909.

Thompson, M. P.; Calkin, D. E.; Finney, M. A.; Ager, A. A.; Gilbertson-Day, J. W. 

2011a. Integrated national-scale assessment of wildfire risk to human and ecological values. Stochastic Environmental Research and Risk Assessment. 25: 761-780.

Thompson, M. P.; Calkin, D. E.; Gilbertson-Day, J. W.; Ager, A. A. 2011b. Advancing 

effects analysis for integrated, large-scale wildfire risk assessment. Environmental 

Monitoring and Assessment. 179: 217-239.
36

Thompson, M. P.; Scott, J. H.; Helmbrecht, D. J.; Calkin, D. E. 2013a. Integrated 

wildfire risk assessment: Framework development and application on the 

Lewis and Clark National Forest in Montana, USA. Integrated Environmental 

Assessment and Management. 9(2): 329-342.

Thompson, M. P.; Scott, J. H.; Langowski, P. G.; Gilbertson-Day, J. W.; Haas, J. 

R.; Bowne, E. M. 2013b. Assessing watershed-wildfire risks on National Forest 

System lands in the Rocky Mountain Region of the United States. Water. 5(3): 

945-971.

Thompson, M. P.; Stonesifer, C. S.; Seli, R. C.; Hovorka, M. 2013c. Developing standardized strategic response categories for fire management units. Fire Management 

Today. 73(1): 18-24.

Turner, M. G.; Romme, W. H. 1994. Landscape dynamics in crown fire ecosystems. 

Landscape Ecology. 9(1): 59-77.
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