**Effect of Thinning and Prescribed Burning on Crown Fire Severity in
Ponderosa Pine Forests**

**Jolie Pollet^1^ and Philip N. Omi^2^**

*^1^Lakeview District Bureau of Land Management and Fremont National
Forest, HC 10 Box 337, Lakeview, Oregon 97630 U.S.A.*

*Tel. 541/947-6175, FAX 541/947-6399 email jolie_pollet@or.blm**.gov*

*^2^Department of Forest Sciences, Colorado State University, Fort
Collins, Colorado 80523 U.S.A.*

*Tel. 970/491-5819, FAX 970/491-6754 email phil@cnr.colostate.edu*

**Abstract**

Fire exclusion policies have affected stand structure and wildfire
hazard in ponderosa pine forests. Wildfires are becoming more severe in
stands where trees are densely stocked with shade-tolerant understory
tree encroachment. Although forest managers have been employing fuel
treatment techniques to reduce wildfire hazard for decades, little
scientific evidence supports such fuel treatments. This research
quantitatively examined fire effects in treated and untreated stands in
western United States National Forests. Four ponderosa pine sites in
Montana, Washington, California and Arizona were selected for study.
Fuel treatments studied include: prescribed fire only, whole-tree
thinning, and thinning followed by prescribed fire. On-the-ground fire
effects were measured in adjacent treated and untreated forests. We
developed *post facto* fire severity and stand structure measurement
techniques to complete field data collection. We found that crown fire
severity was mitigated in stands that had some type of fuel treatment
compared to stands without any treatment. At all four of the sites, the
fire severity and crown scorch was significantly lower at the treated
sites (α=0.03). Results from this research indicate that fuel treatments
removing small diameter trees may be beneficial for reducing crown fire
hazard in ponderosa pine sites.

**Keywords**

*Pinus ponderosa*, Montana, Washington, California, Arizona, fuel
treatment

**Introduction**

Ponderosa forests are the most widely distributed forest type in the
western United States covering millions of hectares (Van Hooser and
Keegan (1988). Fires in ponderosa pine (*Pinus ponderosa*) are becoming
a growing concern (Arno and Brown 1991, Covington and Moore 1994).
Colorado=s Buffalo Creek Fire in 1996 is one example of the recent and
very large wildfires in ponderosa pine that have focused attention on
controlling fire costs and damages (Gale 1977, Gonzalez-Caban 1995).
Over 10,000 acres burned in about 6 hours, mostly as a crown fire
(Orozco 1998). Millions of dollars were spent controlling the Buffalo
Creek Fire, in replacing burned structures, for post-fire rehabilitation
efforts and for consequent flood damage.

Fires in ponderosa pine forests often differ dramatically from those
observed by early settlers. Many of today=s fires are stand-destroying
crown fires as opposed to much lower intensity surface fires (Arno and
Brown 1991, Agee 1993, Covington and Moore 1994, Mutch 1994). In
addition to changes in fire behavior, stand structure in ponderosa pine
forests also has been altered in the last century. Historical accounts
describe large, park-like and open stands (Weaver 1943, Mutch et al.
1993, Covington and Moore 1994) that can be compared to the densely
packed areas currently undergoingstand conversion as shade-tolerant
trees out-compete ponderosa pine regeneration. These changes may be
attributed to effective fire exclusion efforts over the past 100 years.

Forest managers have long contended that stand structural changes can be
linked to more extreme wildfire behavior (Weaver 1943, Biswell 1960,
Cooper 1960, Dodge 1972, VanWagner 1977, Rothermel 1991, McLean 1993,
Fiedler et al. 1995, Williams 1998). For example, shade-tolerant species
and dense regeneration may serve as ladder fuels to move fire into the
tree crowns (Weaver 1943, Dickman 1978, Laudenslayer et al. 1989,
MacCleery 1995). (Ladder fuels provide vertical continuity between the
surface fuels and crown fuels, increasing the likelihood of of torching
and crowning.) Fuel treatments such as prescribed fire and mechanical
thinning are offered as ways to reduce or retard wildfire spread and
intensity in ponderosa pine forests (Weaver 1961, Biswell et al. 1968,
Babbitt 1995).

Many scientists and land managers assume that fuel treatments reduce
wildfire hazard, but few studies have analyzed on-the-ground fire
effects in treated versus untreated stands. Much of the evidence
supporting the effectiveness of fuel treatments in mitigating wildfire
damages has been inferred from informal observation, nonsystematic
inquiry or computer modeling (Omi and Kalabokidis 1991, Edminster and
Olsen 1995, Fiddler et al. 1995, Fiedler 1996, Kalabokidis and Omi 1998,
Scott 1998a, 1998b, Stephens 1998). Only two studies have examined field
wildfire effects in stands with fuel manipulations. First, Vihanek and
Ottmar (1993) measured more severe post-wildfire effects in areas where
slash was left compared to less severe effects in slash-treated areas.
Another study attempted to quantify fire damage to ponderosa pine tree
crowns by examining post-fire aerial photos and available databases
(Weatherspoon and Skinner 1995). Weatherspoon and Skinner found that
sites with harvest treatments that included complete slash removal had
lower fire severity, but they did not complete field verification of the
results. In contrast to these previously mentioned studies, our study
systematically and quantitatively examines field observations following
wildfire in treated versus untreated ponderosa pine stands.

Our hypothesis is that fuel treatments reduce fire severity and crown
scorch. Fire severity, for the purpose of this study, refers to fire=s
effect on the ecosystem and is directly related to post-fire vegetation
survival (Ryan and Noste 1985). Study objectives are to compare crown
scorch and crown consumption in untreated versus treated stands; and
develop a methodology for making *post facto* comparisons of fire
severity in untreated versus treated ponderosa pine stands.

**Methods**

Methods for study site selection and field data collection are described
below. Both site selection and data collection were tailored to assure
study integrity, i.e., eliminate intentional or unintentional bias.

*Site Selection*

During the initial stages of study development, wildfires occurring less
than fifteen years prior that had fuel treatment activities within the
wildfire perimeter were all potential candidates. Selection was
eventually narrowed to those sites with mechanical fuel treatments,
sites that could be sampled before deterioration of wildfire effects,
sites with ponderosa pine as the dominant tree species and sites where
wildfire behavior was not affected by suppression activities. Wildfires
that had accurate pre-fire fuel treatment maps and records were also
favored.

We began field searching for suitable wildfires in 1995 and ended our
search in 1998. During that time, twelve sites were considered for
inclusion in this study. Of those, only four wildfires met our selection
criteria: Webb Fire in Montana; Tyee fire in Washington; Cottonwood Fire
in California; and Hochderffer Fire in Arizona. Table 1 summarizes the
12 fires that were considered for this study, but not selected. Table 1
also provides anecdotal observations on the effectiveness of fuel
treatments.

\[Insert\] Table 1. Candidate fires that were considered but not
selected for this study (Omi 1997). This table provides anecdotal
evidence supporting the benefits of fuel treatments in mitigating
wildfire spread and related damages.

Sites were selected in ponderosa pine forests that had areas of adjacent
untreated and treated stands and that were burned in wildfires. The
following criteria were used to select sites for the study:

-   stands where ponderosa pine is the major species;

-   adjacent treated and untreated stands exposed to the same recent
    wildfire;

-   stands that had accurate treatment records (i.e., maps, timber sale
    inventories); and

-   stands that were treated within 15 years prior to wildfire. In
    ponderosa pine forests, stands that were treated greater than 15
    years prior to wildfire may have out-grown the effects of the fuel
    treatment.

Stands from each category were adjacent to each other to facilitate
comparisons. We avoided selecting sites with confounding influences such
as roads, wide streams or constructed firelines that may have a
significant effect on fire behavior. Since slash resulting from logging
operations increases fire hazard, at least in the short run (Fahnestock
1968, Vihanek and Ottmar 1993), only thinned stands where slash residues
were effectively removed prior to wildfire incidence were considered.

*Field Data Collection*

Selected ponderosa pine stands were categorized as either Atreated@ or
Auntreated@ depending on the presence of a fuel treatment. We consulted
with agency officials and reviewed forest records to determine the
fitness of sites. Adjacent untreated and treated stands were assumed to
be equivalent prior to the treatment. By selecting stands that were
adjacent to each other and on similar topography, we minimized the
differences in weather and topography between the untreated and treated
areas.

The first site, Webb, had a prescribed fire only fuel treatment. (Jolie:
do you want to comment later on the difficulty of sampling prescribed
fire only sites---i.e., since treatment effects such as stumps won't be
apparent?) After sampling on that site, we limited fuel treatments to
some type of mechanical tree removal, with or without subsequent
prescribed fire. We focused the three later sites on mechanical fuel
treatments since prescribed fire was already known to mitigate fire
effects (Wagle and Eakle 1979) and we wanted to narrow the focus of this
study.

Plots located along transects captured the variability in the untreated
and treated areas. We sampled an equal number of plots in the untreated
and treated areas. Transect locations were located based on terrain and
topography, and on the treatment and wildfire boundaries. Depending on
the site, three or four transects that spanned the treated and untreated
areas were situated parallel 150 meters apart. Six to eight plots per
transect were located 150 meters apart. By selecting plot transect
locations prior to any field visits, we avoided locating plots in areas
that would possibly introduce bias. Prior to starting field sampling, we
mapped transects and plot locations on a 72 minute topographic map that
delineated the treated and untreated stands.

We studied modifications of stand structure and canopy characteristics
that are known to mitigate fire hazard. To determine the fuel
treatment=s effect on stand characteristics, three variables describing
stand structure were measured: stand density (trees/hectare), basal area
(meters^2^/hectare) and average diameter (cm) of trees on the plot.
Sample trees were selected using variable plot sampling using a
Acruiser=s crutch@ angle gauge.

Crown characteristics, especially crown bulk density and height to the
live crown, are known to affect crown fire initiation and propagation
(VanWagner 1977, Rothermel 1991). Since crown bulk density estimates
cannot be determined accurately from simple field measurements, crown
weight was used as a substitute for crown bulk density (Brown 1978).
Formulas to determine the crown weight (kg) for the Webb, Tyee and
Cottonwood sites incorporated the ratio of crown to tree height,
diameter at breast height (DBH) and crown position (Brown 1978). DBH was
measured with a metric diameter tape, and crown length and tree height
were calculated from clinometer measurements. Crown position, whether
dominant, co-dominant or intermediate, was recorded for each tree in the
plot. We accounted for the height to live crown factor by incorporating
it into the crown weight formula computed for each tree. At the
Hochderffer site, time constraints precluded crown weight measurements.
By eliminating crown weight data, we could sample more plots over a
shorter period of time. In addition, we collected ample data from the
three previous wildfire sites to test any relationships between crown
weights and severity measurements.

Fire severity was classified by observing foliage scorch and crown
needle consumption (Wagener 1961, Wyant et al. 1986). Crown scorch
percent and crown position were estimated ocularly (Peterson 1985, Wyant
et al. 1986) and, crown scorch height and percent crown scorch
measurements were adapted from Ryan and Noste (1985).

One estimate of fire severity rating per plot was ocularly determined
based mostly on the condition of the aerial fuels. We did not complete a
fire severity estimate from the soil/forest floor organic layer
perspective because the elapsed time since the fire to sampling resulted
in deterioration of much of that evidence. The following severity rating
criteria were adapted from Omi and Kalabokidis (1991):

-   Unburned, fire did not enter the stand (rating=1);

-   Light, surface burn without crown scorch (rating=2);

-   Spotty, irregular crown scorch (rating=3);

-   Moderate, intense burn with complete crown scorch (rating=4);

-   Severe, high intensity burn with crowns totally consumed (rating=5).

We used multi-variate response permutation procedures (MRPP) for
statistically testing differences between the untreated and treated
groups in this study (Mielke 1986, Good 1994). Non-parametric tests,
such as MRPP, have several advantages compared to using more well-known
parametric procedures. While *t*-tests are frequently used for two
sample comparisons, the validity of the assumptions of the *t*-test are
questionable in this study. The various data sets in this study were
relatively small and contained several outliers. MRPP techniques may be
superior to *t*-tests when the sample size is small, if the assumption
of normally distributed populations is not reasonable (i.e., samples
contain extreme values or outliers), and if multivariate comparisons are
desired. For other examples of MRPP used in forestry studies, see
Huckaby and Moir (1995) and Reich (1991).

**Selected Study Site Descriptions**

The four sites we sampled all met the selection criteria, but each site
was unique in terms of stand characteristics, treatment type, and
wildfire behavior. Table 2 summarizes general descriptions for the four
wildfires and treatment types.

\[Insert\] Table 2. Description of sampling sites at the Webb, Tyee,
Cottonwood and Hochderffer wildfires

Figures 1, 2,3 and 4 show the adjacent treated and untreated stands at
the four sampling sites.

\[Insert\] Figure 1. Photos of untreated and treated stands at the Webb
Fire site at adjacent locations.

\[Insert\] Figure 2. Photos of untreated and treated stands at the Tyee
Fire site at adjacent locations.

\[Insert\] Figure 3. Photos of untreated and treated stands at the
Cottonwood Fire site at adjacent locations.

\[Insert\] Figure 4. Photos of untreated and treated stands at the
Hochderffer Fire site at adjacent locations. Multiple stems with full
crowns in the foreground of the treated photo mask the larger diameter
trees in this plot.

**Results**

Tables 3, 4, 5, 6, and 7 summarize the results. Table 3 shows that
post-fire basal area is higher in the untreated plots for all sites
except Cottonwood (see below for further explanation). Slightly higher
basal areas in the treated stands may be explained by understanding that
a stand with many small trees may have similar basal area to a stand
with few large trees.

The number of trees per hectare is much higher in the untreated stands
at all four sites; the untreated Tyee site was especially dense with
1,244 trees per hectare. The average diameter of trees on the plots is
higher for the treated stands which shows that the fuel treatment
removed smaller diameter trees. The crown scorch percent and fire
severity rating are higher for untreated stands at all four sites. The
treated stands had higher crown weights. The formulas for estimating
crown weights (Brown 1978) are most influenced by diameter. Thus, larger
diameter trees, such as those found in the treated stands, will produce
greater crown weights.

Some differences in topography are evident between the untreated and
treated sites. Due to generally more active fire behavior in west-facing
sites compared to northwest aspects, one may expect more severe fire
effects on western aspects. However, at the Tyee site, higher fire
severity was found in plots with a northwest aspect. Further, inspection
of the slope data showed that for two sites the treated areas had
steeper slopes and for the other two sites the untreated area had
steeper slopes.

\[Insert\] Table 3. Key site characteristics for the four wildfires.
(Standard deviations are in parentheses.) Identical superscripts
indicate that the untreated and treated sites are not significantly
different using univariate MRPP, a=.05 (Good 1994). Wildfires with
different superscripts indicate that the sites are significantly
different. Items without superscripts were not tested.

The basal area differences for the Cottonwood site yield some peculiar
results. The two means are almost identical (30.0 versus 30.3 m^2^/ha)
but are significantly different. Examining the Cottonwood site=s density
and tree diameters, the slightly higher basal area in that treated stand
may be attributed to that stand having fewer but larger trees. There are
many outliers at the Cottonwood site and the data ranges are very
different between the untreated and treated plots. Basal areas in the
treated areas ranged from 21 to 39 m^2^/ha compared to the untreated
range of 4 to 60 m^2^/ha. Only 16% (2 observations) of the observations
for the untreated area fell within the range of the treated area. The
likelihood that such extreme values (i.e., basal area \<20 m^2^/ha or
basal area\>40 m^2^/ha) would be observed in the treated plots is very
small. Therefore, the two plots have significantly different basal areas
even though their means are almost identical.

Statistical analysis provided additional insights into structural
differences between treated versus untreated stands. Univariate and
multivariate stand structure comparisons between untreated and treated
plots are analyzed (Tables 4 and 5). Differences in fire severity rating
and percent crown scorch in untreated versus treated plots is presented
statistically using MRPP (Table 6). Lastly, a correlation matrix (Table
7) shows associations between independent variables (density, basal
area, diameter of trees on the plot, crown weight and slope) and the
dependent variables (fire severity rating and crown scorch).

Results indicate that the untreated and treated stands are significantly
different for the Webb, Tyee and Cottonwood sites. The lack of
significant differences among the univariate and multivariate stand
characteristic comparisons for the Hochderffer site is particularly
noteworthy (Tables 4 and 5). Notice, however, the significant
differences at that site for fire severity rating and crown scorch
(Table 6). Something other than stand structure factors likely
contributed to the differences in fire severity. Surface fuel loading or
differences in fuel moistures rather than stand structure may have been
the fire severity driver at this site.

\[Insert\]Table 4. *P*-values for univariate comparisons using MRPP
comparing basal area (m^2^/ha), density (#stems/ha) and diameter (cm)
between treated and untreated plots for the four sites (Good 1994).

\[Insert\] Table 5. Multivariate MRPP comparisons for basal area
(m^2^/ha), density (#stems/ha), and diameter (cm) on the four sites
(Good 1994). These data were standardized \[(x-median)/range\] to
eliminate differences in units.

\[Insert\] Table 6. *P*-values for univariate comparisons using MRPP
comparing fire severity rating and percent crown scorch between
untreated and treated plots for the four sites (Good 1994).

Table 7 illustrates the correlation coefficients showing trends and
relationships among the independent and dependent variables.
Relationships among the independent and dependent variables are the most
interesting and meaningful to this study. Density, basal area, diameter
and crown weight all are significantly correlated with plot severity
rating and percent crown scorch. The highest correlation coefficient
(r=0.57, r^2^=0.32) between the independent and dependent variables is
among density and fire severity rating. Thus 32% of the variation in
fire severity rating can be explained by the variation in density. Slope
does not appear to be related to fire severity or percent crown scorch.

(Jolie: This deletion could be moved to the Discussion, if you wish)

\[Insert\] Table 7. Summary of correlation coefficients (r) for Webb,
Tyee, Cottonwood and Hochderffer sites for fire damage/severity
variables (fire severity rating and percent crown scorch), stand
structure variables (density, basal area, average diameter of trees on
the plot and crown weight) and slope. *P*-values are in parentheses.

**Discussion**

The treated plots in this study have lower fire severity ratings and
less crown scorch than the untreated plots. The null hypothesis (H~o~),
that both fire severity and crown scorch each do not differ
significantly among untreated and treated plots, is rejected in favor of
the research hypothesis (H~a~), that fire severity and crown scorch are
higher in untreated plots.

From these results we infer that the types of fuel treatments studied
reduce fire severity rating and crown scorch. Based on the statistical
results and field reconnaissance, sites with mechanical fuel treatment
appear to have more dramatically reduced fire severity compared to sites
with prescribed fire only. Although fire severity ratings and percent
crown scorch are significantly different for untreated versus treated
plots at all sites (Tables 3 and 6), the Webb site=s differences were
the least extreme. Apparently, mechanical fuel treatments at the Tyee,
Cottonwood and Hochderffer sites allow for more precise and controlled
results compared to prescribed fire. For example, mechanical fuel
treatment programs may specify the exact number of post-treatment
residual trees per hectare and the treatment can be applied uniformly
across the stand. By contrast, prescribed fire fuel treatment often
varies across a stand and results in less precise stand structure
changes.

For the Webb, Tyee and Cottonwood sites, the stand characteristics
contributed to the differences in fire severity. The fuel treatments at
these three sites resulted in forests with much lower density and larger
trees. Stands with fewer trees have less continuous crown and ladder
fuels. Larger trees generally have crowns higher off the ground and have
thicker bark which makes them more fire resistant. This twofold benefit
of treated stands results in lower potential for crown fire initiation
and propagation and for less severe fire effects.

Stand structure for the Hochderffer site is not significantly different
among the treated and untreated stands; other factors contributed to
less severe fire effects in the treated stands since fire severity and
percent crown scorch differences cannot be explained by stand structure
manipulations. We did not study two factors that are known to drive fire
behavior: surface dead downed fuel loading and fuel moistures. Due to
the *post facto* nature of this study, we could not adequately quantify
pre-fire fuel loadings or fuel moistures. Although fuel loading was not
quantified in this study, we assume that the studied fuel treatments
reduce surface fuel loading. For example, a recent prescribed burn will
reduce surface fuel loading in the short-run. The Hochderffer site had a
recent prescribed burn after the mechanical thinning treatment.
Therefore, it is likely that surface fuel loading contributed to less
severe fire effects in the treated stands at the Hochderffer site.

Fuel moistures may be affected by microclimate and probably do vary
between the untreated and treated stands. A more open stand allows more
wind and solar radiation resulting in a drier microclimate compared to a
closed stand. A drier microclimate generally contributes to more severe
fire behavior. However, our study does not support the assertion that
more open stands experience higher fire severity. More open stands had
significantly less fire severity compared to the more densely stocked
untreated stands in this study. The degree of openness in the studied
treated stands may not have been sufficient to increase fire activity.

Density and average tree diameter are closely related to fire severity
(Table 7). Based on this study=s results, removing small diameter trees
from a ponderosa pine stand reduces subsequent wildfire severity. At the
four sites, the fuel reduction overcomes the microclimate=s effect on
fire behavior. These findings agree with years of forest managers= field
observations.

Many fire scientists have put much effort toward crown fire modeling and
refinements are continually emerging (VanWagner 1977, Rothermel 1991,
Agee 1996, Scott 1998c). Because crown fire modeling is such a
complicated and heavily studied topic, we felt this paper would not
provide unique insight into modeling procedures. Correlations between
the independent variables (the stand characteristic variables) and the
dependent variables (crown scorch and fire severity rating) indicate
relationships and trends that reinforce understanding of crown fire and
fire effects.

Critics of this research may consider alternative explanations for our
study=s differences in fire severity: that the variability in fire
effects is related to the random nature of wildfire behavior and not due
to the stand=s fuel manipulation. In other words, the results were
obtained by chance alone. All the interacting factors that govern the
spread and intensity of wildfires are not clearly understood. The ways
fire burn are influenced by multitudes of factors, some of which have
been studied and measured and other factors have not. This study aimed
to reduce influences other than fuel treatments in order to test the
effectiveness of those treatments for reducing wildfire severity.
Although we were unable to capture all of the subtleties that govern
fire behavior and severity, the evidence is convincing that the
treatment mitigates wildfire severity by whatever means. For three of
the four study areas, stand structure differences between treated and
untreated stands account for differences in fire severity (Table 5). And
there is clear evidence that fuel treatments in this study reduce
wildfire severity (Table 6) at all four sites. For the first time the
assertion that fuel treatments reduce wildfire severity has been tested
and analyzed.

Site selection was a significant and time-consuming aspect of this
study. Most of the wildfires (8 of 12) that we seriously considered did
not meet the specified criteria. Several of the proposed sites also had
a surprising lack of accurate forest records that precluded sampling.
For example, forest managers knew of fuel treatments in areas burned by
subsequent wildfire, but had little data to confirm the treatment
locations or the exact nature of the fuel treatment.

Sites that had fuel treatments could not be selected in advance since a
wildfire had to take place before a treatment could be studied.
Therefore, all measurements were taken after wildfire occurrence.
Developing and synthesizing methods for assessing *post facto* fire
effects that may be useful for similar studies elsewhere was an
important aspect of this research. Problems involved with a *post facto*
analysis and ways the problems were addressed include:

-   [Wildfire events cannot be predicted in advance and quantitative
    pre-wildfire site information is often unavailable. Data may be
    lacking to quantify the fuel treatment to the degree desirable for a
    scientific analysis]{.underline} We developed sampling methods that
    allowed for quantification of the pre-wildfire condition in terms
    of: density, basal area, size of the trees, and crown weights.
    Additionally, we only chose sites that had sufficient fuel treatment
    data prior to the wildfire.

-   [Confounding influences may cloud observed wildfire
    effects.]{.underline} These include: suppression activities
    disturbing the landscape and retarding wildfire spread; roads
    affecting fire spread; and salvage activities obliterating fire
    severity evidence.

-   [Difficulty in minimizing the variability across and among the
    sites. Variability may mask treatment effects.]{.underline} We
    selected stands according to pre-specified conditions, including:
    ponderosa pine dominance; similar aspect for treated and untreated
    stands; treated and untreated stands that are adjacent; and treated
    stands that were treated 10 years or less before the wildfire. We
    selected sites that were adjacent with similar aspect and topography
    to minimize other fire behavior influences. Adjacent sites are also
    likely to have similar soils, similar vegetation types and similar
    weather patterns. These similarities reduce the possibility that
    factors other than the fuel treatment are the major determinants of
    fire severity.

-   [Time since wildfire must be relatively short; otherwise evidence
    may be lost or erased (i.e., ground char, crown
    scorch/consumption).]{.underline} We kept the time since wildfire at
    three years or less. The Webb, Tyee and Hochderffer sites were
    sampled only one year post-wildfire and had more preserved evidence
    compared to the Cottonwood site that was sampled two years
    post-wildfire.

The best methods for assessing fire severity accurately require
observing an active fire=s behavior or by immediate post-fire
observation. That way, real-time fire behavior may be measured instead
of estimated and there is no removal of the remaining evidence. Due to
the unpredictable nature of wildfire timing and locations it was
impossible for this study to take real-time fire behavior measurements
or make immediate post-fire observations. We encountered delays of one
year or more between the wildfire=s occurrence and field sampling. It
took several months after a wildfire occurred for land managers to learn
of this study, arrange a site visit and determine if a site met the
selection criteria. Time was also lost gathering records and waiting for
suitable weather for sampling.

Although several problems associated with a *post facto* study have been
highlighted, few feasible study design alternatives exist. Researchers
could potentially concentrate on quantifying fuels and fuel treatments
over an area large enough to contain ample fuel treatment and control
plots. A prescribed burn could then be conducted to examine the effects
the treatment had on fire severity. However, it is difficult to
replicate the scale and weather of a crowning wildfire. Another
alternative is to pre-select an area and complete a careful fuel
treatment program where all conceivable variables affecting fire
behavior are measured pre-fire. Monitoring equipment could be set up for
measuring wildfire observations. Then, researchers could wait for a
wildfire to burn the entire area. However, these alternative methods are
too impractical to successfully implement. Other studies have carefully
quantified fuel treatments and used standard computer fire behavior
modeling to determine the fuel treatment effects on fire behavior
(Kalabokidis and Omi 1998, Scott 1998a, Stephens 1998). Computer
simulations avoid problems associated with a *post facto* field study,
but computer simulation is not always a good substitute for describing
actual fire behavior.

**Conclusions and Management Implications**

Our findings indicate that fuel treatments do mitigate fire severity.
Treatments provide a window of opportunity for effective fire
suppression and protecting high-value areas. Although topography and
weather may play a more important role than fuels in governing fire
behavior (Bessie and Johnson 1995), topography and weather cannot be
realistically manipulated to reduce fire severity. Fuels are the leg of
the fire environment triangle (Countryman 1972) that land managers can
change to achieve desired post-fire condition. However, in extreme
weather conditions, such as drought and high winds, fuel treatments may
do little to mitigate fire spread or severity.

This study shows that fuel treatments are effective in reducing severity
in short fire-return interval ecosystems. However, fuel treatments in
long fire-return interval ecosystems may be less effective. Most
ponderosa pine forests have adapted to recurring, low severity fire.
Wildfires in lodgepole pine forests, by contrast, are comparatively
infrequent and have high severity. Due to the historical differences in
wildfire frequency and severity in these types of forests, the
effectiveness of fuel treatments in long fire return-interval ecosystems
remains unclear.

Traditional fuel treatment programs were completed on distinct and often
disjointed units, instead of on a landscape-level scale. In order to
lessen the probability of a high-severity wildfire over a landscape, an
entire landscape should be analyzed for determining the most appropriate
scales and locations for fuel treatments. Intensively treating most of a
landscape may not be necessary, but treating strategically located
stands for fuel treatment or treating strips of fuels may be beneficial
for reducing severe wildfire potential across a large area. The scale of
fuel treatments likely affects their efficacy. For example, Tyee fire
observers reported that in at least one instance the fire took over 100
meters to drop out of the crowns when it encountered the fuel treatment
area. Many sites had similar fire severity in treated and untreated
plots, especially near the treatment boundary. If only a small area was
treated, a high-intensity crown fire may have enough momentum to burn
right through a treated area. Mutch and Cook (p. 9, 1996) emphasize
Aprescribed fire has not been used on a scale adequate for sustaining
the productivity of fire-dependent ecosystems.@

There are at least three ways to reduce tree densities and accomplish
fuel treatments: wildfire, prescribed fire and mechanical thinning. The
first, natural fires, are often impractical. Letting natural fires play
their historical role may have unwanted effects in forests that have
undergone major stand structural changes over the past years of fire
exclusion. Any fire started may result in historically uncharacteristic
high severity. In many ponderosa pine forests choked with dense,
small-diameter trees, or encroached by shade-tolerant trees, natural
fires may no longer play a strategic role.

The second strategy for restoring these forests is large-scale
prescribed burning. This is likely to be effective in stands that have
moderate or low tree densities, little encroachment of ladder fuels,
moderate to steep slopes which preclude mechanical treatment, and
expertise in personnel to plan and implement such large prescribed
burns. Large-scale implementation of this strategy will require funding
for the planning and implementation over current expenditures and may
require modifications to current air quality legislation. Future results
of such expenditures may be seen down the road in lessened wildfire
suppression costs, reduced fire severity, and reduced air quality
impacts.

Mechanical tree removal, the third strategy, works best on forests that
are too densely packed to burn, that have nearby markets for
small-diameter trees, and areas where expertise and personnel are not
available for prescribed burning programs. Mechanical tree removal may
be accomplished by many different types of harvest, including
precommercial thinning, selection or shelterwood harvest coupled with
small-diameter tree removal, and thinning from below (Fiedler 1996). The
goal is to manage forests for much lower tree densities leaving larger
residual trees. Harvests to reduce wildfire hazard will remove
small-diameter trees in contrast to traditional timber harvests.
Mechanical fuel treatments can be very labor intensive, especially on
steep slopes and in remote areas, and may not be commercially attractive
due to the small diameter trees that need removal. To make fuel
treatments more cost-effective for small-diameter trees, consistent
markets are necessary (Nakamura 1996). Fiedler et al. (1997) assert that
mechanized tree harvest on moderately-steep terrain coupled with removal
of large amounts of biomass can generate considerable revenue. Periodic
underburns and programs for restoring natural fire are critical to
maintain these post-harvest stands.

Fuel treatment programs may be costly and time-consuming. But wildfire
problems aren=t going away soon. We suggest focusing programs, funding
and management attention where the risk resulting from severe wildfire
is greatest: urban-interface, tree plantations, critical watersheds and
habitat for threatened and endangered species. Treating high-volume
areas using mechanized equipment could offset costs associated with fuel
removal on steep slopes with little timber. Costs associated with
wildfire suppression, in terms of funding suppression efforts and
personal safety, far outweigh the costs of fuel treatment on similar
landscapes.

**Acknowledgements**

The authors would like to thank the following people for their help in
facilitating this research: Steve Arno retired from the Rocky Mountain
Research Station, Fire Sciences Laboratory; Ron Hvizdak, Kootenai
National Forest; Michelle Ellis, Wenatchee National Forest; Scott
Abrams, Tahoe National Forest; Allen Farnsworth, Coconino National
Forest; Lyn Morelan, Boise National Forest; and Robin Reich, Colorado
State University.

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Table 1. Candidate fires that were considered but not selected for this
study during 1992-95 (Omi 1997).

  ------------------- -------------------- -------------------- ------------------------ --------------------------------------
  [Fire               [Year]{.underline}   [Size]{.underline}   [Location]{.underline}   [Description]{.underline}
  Name]{.underline}                                                                      

  Cleveland           1992                 n/a                  Eldorado National        Ponderosa pine plantations previously
                                                                Forest, CA               underburned survived a wildfire when
                                                                                         suppression crews were able to
                                                                                         backfire from the treated areas. This
                                                                                         site was not selected due to
                                                                                         suppression activities near the
                                                                                         treatment boundary.

  Paddock             1992                 12 ha                Lakeview, OR             Fire spread into ponderosa pine that
                                                                                         was underburned 3 years prior to
                                                                                         wildfire. The fire had potential to
                                                                                         reach 2,000 ha and spread on to
                                                                                         private land. Land managers felt that
                                                                                         the treatment limited the wildfire
                                                                                         size, resource damage and suppression
                                                                                         costs. This site was not selected
                                                                                         because it did not have mechanical
                                                                                         fuel treatment. We later limited
                                                                                         studied fuel treatments to some type
                                                                                         of mechanical treatment.

  Star Gulch          1992                 12,000 ha            Boise National Forest,   Ponderosa pine plantations that were
                                                                ID                       thinned and underburned survived a
                                                                                         wildfire. Untreated plantations
                                                                                         experienced high mortality. The time
                                                                                         lapse between the wildfire and study
                                                                                         notification was too long for this
                                                                                         study to be included since there was
                                                                                         much deterioration of fire effects
                                                                                         evidence.

  Aspen               1994                 664 ha               Salyer National Wildlife An escaped prescribed fire in
                                                                Refuge, SD               aspen-grassland-shrubland became
                                                                                         controllable in an aspen clear-cut.
                                                                                         Ponderosa pine was not a dominant
                                                                                         species.

  Henry Peaks         1994                 3,240 ha             Flathead Reservation, MT An area thinned 20 years prior to the
                                                                                         wildfire by uneven-aged logging (whole
                                                                                         tree skidding with pile burning)
                                                                                         experienced significantly lower fire
                                                                                         severity and mortality compared to
                                                                                         adjacent forest. The length of time
                                                                                         since treatment precluded this site=s
                                                                                         selection.

  LeClair             1994                 13,355 ha            Warm Springs             Dozer line and prescribed burning one
                                                                Reservation, OR          year prior to wildfire in
                                                                                         sagebrush-grass held the fire at a
                                                                                         subdivision boundary. Clear-cutting as
                                                                                         a fuel treatment did not meet the
                                                                                         study=s objectives.

  Robinson            1994                 3,400 ha             Yellowstone National     Beetle-killed lodgepole pine
                                                                Park, WY                 (self-thinned to lower density)
                                                                                         experienced significantly lower fire
                                                                                         severity compared to adjacent burned
                                                                                         areas. The dominant vegetation was not
                                                                                         ponderosa pine and it was a naturally
                                                                                         treated stand, not a mechanical fuel
                                                                                         treatment.

  Wind                1995                 40 ha                Deschutes National       Fire behavior became more controllable
                                                                Forest, OR               in a grass and rabbitbrush area
                                                                                         treated by prescribed fire in 1987.
                                                                                         This enabled a dozer line to contain
                                                                                         the wildfire. The dominant vegetation
                                                                                         was not ponderosa pine.
  ------------------- -------------------- -------------------- ------------------------ --------------------------------------

Table 2. Description of sampling sites at the Webb, Tyee, Cottonwood and
Hochderffer wildfires.

+------------+-------------+-------------+-------------+-------------+
|            | Fire        |             |             |             |
+------------+-------------+-------------+-------------+-------------+
|            | Webb        | Tyee        | Cottonwood  | Hochderffer |
+------------+-------------+-------------+-------------+-------------+
| Treatment  | broadcast   | pr          | whole tree  | u           |
| Type       | burn in     | ecommercial | thinning in | ndetermined |
|            | 1989        | thinning in | 1989, 1990  | tree        |
|            |             | 1970s with  |             | harvest in  |
|            |             | underburn   |             | 1970s with  |
|            |             | for slash   |             | broadcast   |
|            |             | removal in  |             | burn in     |
|            |             | 1983        |             | 1995        |
+------------+-------------+-------------+-------------+-------------+
| Date of    | September,  | August,     | August,     | June, 1996  |
| Fire       | 1994        | 1994        | 1994        |             |
+------------+-------------+-------------+-------------+-------------+
| Date       | July, 1995  | October,    | September,  | October,    |
| Sampled    |             | 1995        | 1996        | 1997        |
+------------+-------------+-------------+-------------+-------------+
| Size of    | 1,415 ha    | 56,780 ha   | 18,620 ha   | 6,640 ha    |
| Fire       |             |             |             |             |
+------------+-------------+-------------+-------------+-------------+
| Elevation  | 1,067 m     | 762 m       | 2,012 m     | 2,408 m     |
+------------+-------------+-------------+-------------+-------------+
| Aspect     | south       | west        | west        | north       |
+------------+-------------+-------------+-------------+-------------+
| National   | Kootenai    | Wenatchee   | Tahoe NF,   | Coconino    |
| Forest     | NF, Montana | NF,         |             | NF,         |
| Location   |             | Washington  | California  |             |
|            |             |             |             | Arizona     |
+------------+-------------+-------------+-------------+-------------+

Table 3. Key site characteristics for the 4 wildfires. (Standard
deviations are in parentheses.) Identical superscripts indicate that the
untreated and treated sites are not significantly different using
univariate MRPP, α=.05 (Good 1994). Wildfires with different
superscripts indicate that the sites are significantly different. Items
without superscripts were not tested.

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 11%" />
<col style="width: 7%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 9%" />
<col style="width: 10%" />
<col style="width: 10%" />
<col style="width: 9%" />
<col style="width: 8%" />
<col style="width: 8%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td></td>
<td><p>Sam-</p>
<p>ple Size</p></td>
<td>Aspect</td>
<td>Slope (%)</td>
<td>Basal Area (m<sup>2</sup>/ha)</td>
<td><p>Density</p>
<p>(stems/ha)</p></td>
<td><p>Avg. Diameter</p>
<p>(cm)</p></td>
<td>Fire Severity Rating</td>
<td>Crown Scorch (%)</td>
<td>Crown Weight (kg)</td>
</tr>
<tr class="even">
<td rowspan="2"><blockquote>
<p>Webb</p>
</blockquote></td>
<td>Untreated</td>
<td>9</td>
<td>S</td>
<td><p>29<sup>a</sup></p>
<p>(13)</p></td>
<td><p>23.0<sup>a</sup></p>
<p>(13.9)</p></td>
<td><p>637<sup>a</sup></p>
<p>(498)</p></td>
<td><p>24.1<sup>a</sup></p>
<p>(10.5)</p></td>
<td><p>3.2<sup>a</sup></p>
<p>(0.8)</p></td>
<td><p>67<sup>a</sup></p>
<p>(33)</p></td>
<td><p>165<sup>a</sup></p>
<p>(179)</p></td>
</tr>
<tr class="odd">
<td>Treated</td>
<td>9</td>
<td>S</td>
<td><p>39<sup>a</sup></p>
<p>(9)</p></td>
<td><p>14.7<sup>a</sup></p>
<p>(9.4)</p></td>
<td><p>73<sup>b</sup></p>
<p>(42)</p></td>
<td><p>43.1<sup>b</sup></p>
<p>(20.0)</p></td>
<td><p>2.6<sup>b</sup></p>
<p>(0.5)</p></td>
<td><p>26<sup>b</sup></p>
<p>(34)</p></td>
<td><p>334<sup>a</sup></p>
<p>(197)</p></td>
</tr>
<tr class="even">
<td rowspan="2"><blockquote>
<p>Tyee</p>
</blockquote></td>
<td>Untreated</td>
<td>9</td>
<td>NW</td>
<td><p>38<sup>a</sup></p>
<p>(17)</p></td>
<td><p>24.6<sup>a</sup></p>
<p>(12.8)</p></td>
<td><p>1244<sup>a</sup></p>
<p>(1417)</p></td>
<td><p>20.7<sup>a</sup></p>
<p>(13.6)</p></td>
<td><p>4.4<sup>a</sup></p>
<p>(0.5)</p></td>
<td><p>100<sup>a</sup></p>
<p>(0)</p></td>
<td><p>63<sup>a</sup></p>
<p>(71)</p></td>
</tr>
<tr class="odd">
<td>Treated</td>
<td>9</td>
<td>W</td>
<td><p>22<sup>a</sup></p>
<p>(11)</p></td>
<td><p>15.8<sup>a</sup></p>
<p>(5.6)</p></td>
<td><p>218<sup>b</sup></p>
<p>(98)</p></td>
<td><p>30.7<sup>b</sup></p>
<p>(0.7)</p></td>
<td><p>3.0<sup>b</sup></p>
<p>(0.0)</p></td>
<td><p>74<sup>b</sup></p>
<p>(17)</p></td>
<td><p>142<sup>b</sup></p>
<p>(75)</p></td>
</tr>
<tr class="even">
<td rowspan="2"><blockquote>
<p>Cotton-</p>
<p>wood</p>
</blockquote></td>
<td>Untreated</td>
<td>12</td>
<td>W</td>
<td><p>21<sup>a</sup></p>
<p>(14)</p></td>
<td><p>30.0<sup>a</sup></p>
<p>(19.7)</p></td>
<td><p>578<sup>a</sup></p>
<p>(571)</p></td>
<td><p>31.3<sup>a</sup></p>
<p>(11.7)</p></td>
<td><p>4.0<sup>a</sup></p>
<p>(0.8)</p></td>
<td><p>78<sup>a</sup></p>
<p>(32)</p></td>
<td><p>194<sup>a</sup></p>
<p>(146)</p></td>
</tr>
<tr class="odd">
<td>Treated</td>
<td>12</td>
<td>S</td>
<td><p>11<sup>b</sup></p>
<p>(5)</p></td>
<td><p>30.3<sup>b</sup></p>
<p>(5.5)</p></td>
<td><p>262<sup>b</sup></p>
<p>(184)</p></td>
<td><p>39.8<sup>b</sup></p>
<p>(8.4)</p></td>
<td><p>2.7<sup>b</sup></p>
<p>(0.6)</p></td>
<td><p>26<sup>b</sup></p>
<p>(27)</p></td>
<td><p>225<sup>a</sup></p>
<p>(210)</p></td>
</tr>
<tr class="even">
<td rowspan="2"><blockquote>
<p>Hoch-</p>
<p>derffer</p>
</blockquote></td>
<td>Untreated</td>
<td>12</td>
<td>N</td>
<td><p>11<sup>a</sup></p>
<p>(6)</p></td>
<td><p>25.0<sup>a</sup></p>
<p>(8.1)</p></td>
<td><p>765<sup>a</sup></p>
<p>(441)</p></td>
<td><p>21.9<sup>a</sup></p>
<p>(7.0)</p></td>
<td><p>4.4<sup>a</sup></p>
<p>(0.6)</p></td>
<td><p>99<sup>a</sup></p>
<p>(1)</p></td>
<td rowspan="2">NOT MEAS-URED</td>
</tr>
<tr class="odd">
<td>Treated</td>
<td>12</td>
<td>N</td>
<td><p>13<sup>b</sup></p>
<p>(6)</p></td>
<td><p>23.3<sup>a</sup></p>
<p>(11.0)</p></td>
<td><p>556<sup>a</sup></p>
<p>(410)</p></td>
<td><p>24.2<sup>a</sup></p>
<p>(6.5)</p></td>
<td><p>2.1<sup>b</sup></p>
<p>(1.0)</p></td>
<td><p>29<sup>b</sup></p>
<p>(38)</p></td>
</tr>
</tbody>
</table>

Table 4. *P*-values for univariate comparisons using MRPP comparing
basal area (m^2^/ha), density (#stems/ha) and diameter (cm) between
treated and untreated plots for the four sites (Good 1994).

  ------------------- ------------------- -------------- ----------------
                      Basal Area          Density        Diameter

  Webb                0.37                0.01\*         0.01\*

  Tyee                0.14                0.01\*         0.02\*

  Cottonwood          0.04\*              0.05\*         0.03\*

  Hochderffer         0.49                0.32           0.45
  ------------------- ------------------- -------------- ----------------

\* Indicates that the treated and untreated plots are significantly
different, α=.05.

Table 5. Multivariate MRPP comparisons for basal area (m^2^/ha), density
(#stems/ha), and diameter (cm) on the four sites (Good 1994). These data
were standardized \[(x-median)/range\] to eliminate differences in
units.

  ---------------------------------------- ------------------------------
                                           p-value

  Webb                                     0.01\*

  Tyee                                     0.02\*

  Cottonwood                               0.02\*

  Hochderffer                              0.96
  ---------------------------------------- ------------------------------

\* Indicates that the treated and untreated plots are significantly
different, α=.05.

Table 6. *P*-values for univariate comparisons using MRPP comparing fire
severity rating and percent crown scorch between untreated and treated
plots for the four sites (Good 1994).

  ---------------- ------------------------- ----------------------------
                   Fire Severity Rating      Percent Crown Scorch

  Webb             0.01\*                    0.03\*

  Tyee             0.01\*                    0.01\*

  Cottonwood       0.01\*                    0.01\*

  Hochderffer      0.01\*                    0.01\*
  ---------------- ------------------------- ----------------------------

\* Indicates that the treated and untreated plots are significantly
different, α=.05.

Table 7. Summary of correlation coefficients (r) for Webb, Tyee,
Cottonwood and Hochderffer sites for fire damage/severity variables
(fire severity rating and percent crown scorch), stand structure
variables (density, basal area, average diameter of trees on the plot
and crown weight) and slope. *P*-values are in parentheses.

+-------------+-----+--------+-------+-------+-------+-------+-------+
|             | DEP |        | I     |       |       |       |       |
|             | END |        | NDEPE |       |       |       |       |
|             | ENT |        | NDENT |       |       |       |       |
|             | VAR |        | VARI  |       |       |       |       |
|             | IAB |        | ABLES |       |       |       |       |
|             | LES |        |       |       |       |       |       |
+-------------+-----+--------+-------+-------+-------+-------+-------+
|             | F   | P      | De    | Basal | Dia   | Crown | Slope |
|             | ire | ercent | nsity | Area  | meter | Weigh |       |
|             | Se  | Crown  |       |       |       | t^\*^ |       |
|             | ver | Scorch |       |       |       |       |       |
|             | ity |        |       |       |       |       |       |
|             | Rat |        |       |       |       |       |       |
|             | ing |        |       |       |       |       |       |
+-------------+-----+--------+-------+-------+-------+-------+-------+
| Fire        | 1   | 0.79   | 0.57  | 0.40  | -0.48 | -0.40 | 0.09  |
| Severity    | .00 |        |       |       |       |       |       |
| Rating      |     | (\     | (\<0  | (\<0  | (\<0  | (\<0  | (     |
|             |     | <0.01^ | .01^\ | .01^\ | .01^\ | .01^\ | 0.43) |
|             |     | \*\*^) | *\*^) | *\*^) | *\*^) | *\*^) |       |
+-------------+-----+--------+-------+-------+-------+-------+-------+
| Percent     |     | 1.00   | 0.45  | 0.26  | -0.45 | -0.43 | 0.12  |
| Crown       |     |        |       |       |       |       |       |
| Scorch      |     |        | (\<0  | (0    | (\<0  | (\<0  | (     |
|             |     |        | .01^\ | .03^\ | .01^\ | .01^\ | 0.28) |
|             |     |        | *\*^) | *\*^) | *\*^) | *\*^) |       |
+-------------+-----+--------+-------+-------+-------+-------+-------+
| Density     |     |        | 1.00  | 0.56  | -0.63 | -0.46 | 0.02  |
|             |     |        |       |       |       |       |       |
|             |     |        |       | (\<0  | (\<0  | (\<0  | (     |
|             |     |        |       | .01^\ | .01^\ | .01^\ | 0.88) |
|             |     |        |       | *\*^) | *\*^) | *\*^) |       |
+-------------+-----+--------+-------+-------+-------+-------+-------+
| Basal       |     |        |       | 1.00  | -0.11 | -0.16 | -0.05 |
|             |     |        |       |       |       |       |       |
| Area        |     |        |       |       | (     | (     | (     |
|             |     |        |       |       | 0.34) | 0.22) | 0.67) |
+-------------+-----+--------+-------+-------+-------+-------+-------+
| Diameter    |     |        |       |       | 1.00  | 0.86  | 0.05  |
|             |     |        |       |       |       |       |       |
|             |     |        |       |       |       | (\<0  | (     |
|             |     |        |       |       |       | .01^\ | 0.67) |
|             |     |        |       |       |       | *\*^) |       |
+-------------+-----+--------+-------+-------+-------+-------+-------+
| Crown       |     |        |       |       |       | 1.00  | -0.03 |
| Weight^\*^  |     |        |       |       |       |       |       |
|             |     |        |       |       |       |       | (     |
|             |     |        |       |       |       |       | 0.82) |
+-------------+-----+--------+-------+-------+-------+-------+-------+

\*Crown Weight was computed for Webb, Tyee and Cottonwood sites only.

\*\* Indicates that the correlation coefficients are significantly
different from 0, α=.05.