United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Research Paper

RMRS-RP-103WWW

June 2013

Fuel Treatments and Fire 

Severity: A Meta-Analysis

Erik J. Martinson and Philip N. Omi
Publishing Services

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526

Martinson, Erik J.; Omi, Philip N. 2013. Fuel treatments and fire severity: A metaanalysis. Res. Pap. RMRS-RP-103WWW. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 38 p.

Abstract

We employed meta-analysis and information theory to synthesize findings reported in the 

literature on the effects of fuel treatments on subsequent fire intensity and severity. Data 

were compiled from 19 publications that reported observed fire responses from 62 treated 

versus untreated contrasts. Effect sizes varied widely and the most informative grouping of 

studies distinguished three vegetation types and three types of fuel treatment. The resultant meta-analytic model is highly significant (p<0.001) and explains 78% of the variability 

in reported observations of fuel treatment effectiveness. Our synthesis highlights several 

considerations that both support and inform the current fuels management paradigm.

Keywords: thinning, prescribed burning, wildfire, effect size, synthesis

Authors

Erik J. Martinson is a Consultant and Partner at MarLynn Ecological Consulting in Fort 

Collins, Colorado.

Philip N. Omi is a Professor Emeritus at Colorado State University and Principal of Omi 

Associates in Sebastopol, California.
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 1

Introduction _____________________________________

Changes in land use and management over the last century or more have increased the vertical and horizontal continuity of wildland fuels in many areas 

of North America (Pyne 1982, but see Keeley and Fotheringham [2001] and 

Johnson and others [2001] for discussion of exceptions). This increase in fuel 

hazard is compounding with climate change (Brown and others 2004) and exurban development (Cova and others 2004) to place ever more values at risk to 

wildfire damages. While the problem has been recognized for decades (Cooper 

1960), political and land management attention on mitigating hazardous fuels 

has recently increased (Stephens and Ruth 2005), piquing an interest for more 

research into the effects and effectiveness of these activities (Botti and others 

1998). Consequently, the volume of fuel treatment studies has expanded greatly 

over the past decade (figure 1), spawning a number of literature reviews to synthesize findings (Hudak and others 2011; table 1).

Figure 1—Cumulative number of fuel treatment effectiveness publications by year and type of study:

1A = observed wildfire response to actual fuel treatment, 1B = 

simulated wildfire response to actual fuel treatment, 2B = simulated 

wildfire response to hypothetical fuel treatment.

Fuel Treatments and Fire 

Severity: A Meta-Analysis

Erik J. Martinson and Philip N. Omi
2 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

A paradigm for fuels management has emerged from these reviews that emphasizes the importance of first distinguishing ecosystems where fire was historically 

frequent and benign while limited primarily by fuel quantity (such as dry conifer 

forests) from those where fire was historically infrequent and stand-replacing 

while limited more by climate (such as sub-arctic and sub-alpine forests or 

California chaparral). The paradigm suggests that large-scale fuels management 

may be inappropriate and counter-productive in the latter types of ecosystems 

(Johnson and others 2001; Keeley and Fotheringham 2001), but should be successful in the former to the degree that more resilient conditions result from 

reducing surface fuels, removing ladders, opening canopies, and selecting for 

fire resistance (such as by leaving large trees), in that order (Agee and Skinner 

2005). Keeley and others (2009) noted that empirical studies in lower-elevation 

western conifer forests consistently demonstrate reduced wildfire severity from 

combinations of thinning and burning, but caution that the slash produced by 

thinning will exacerbate fire hazard until it is also treated. Guidance is less clear 

for ecosystems where the interactions among fire, weather, and fuels are more 

complex and the historic fire regime was a mixture of frequencies and severities (such as mesic mixed forests at middle elevations and latitudes). Thus, the 

recommendation for fuels management in these systems has been for limited 

and cautious application (Schoennagle and others 2004).

However, traditional literature reviews are inherently qualitative in their syntheses of the information provided in research reports (Cooper and others 2009). 

They are also prone to bias in selection and interpretation of findings and tend to 

over-emphasize contradictory conclusions with inadequate attention to sources 

Table 1—Recent literature reviews related to fuel management that were used to identify studies to consider 

for inclusion in our synthesis. The Joint Fire Science Program database of final reports for completed projects (www.firescience.org) was also searched.

1. Greenlee, J.M.; Sapsis, D.B. 1996. Prefire effectiveness in fire management: A summary and a review 

of the state-of-knowledge. Fairfield, WA: International Association of Wildland Fire.

2. Carey, H.; Schumann, M. 2003. Modifying wildfire behavior—the effectiveness of fuel treatments. 

Santa Fe, NM: National Community Forestry Center Southwest Region Working Paper #2.

3. Martinson, E.J.; Omi, P.N. 2003. Performance of fuel treatments subjected to wildfires. USDA Forest 

Service Proceedings RMRS-P-29: 7-14.

4. Fernandes, P.M.; Botelho, H.S. 2003. A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire 12: 117-128.

5. Graham, R.T.; McCaffrey, S.; Jain, T.B. 2004. Science basis for changing forest structure to modify 

wildfire behavior and severity. USDA Forest Service General Technical Report RMRS-GTR-120: 143.

6. Agee, J.K.; Skinner, C.N. 2005. Basic principles of forest fuel reduction treatments. Forest Ecology 

and Management 211: 83-96.

7. Peterson, D.L.; Johnson, M.C.; Agee, J.K.; Jain, T.B.; McKenzie, D.; Reinhardt, E.D. 2005. Forest 

structure and fire hazard in dry forests of the Western United States. USDA Forest Service General 

Technical Report PNW-GTR-628: 1-30.

8. Keeley, J.E.; Aplet, G.H.; Christensen, N.L.; Conard, S.C.; Johnson, E.A.; Omi, P.N.; Peterson, D.L.; 

Swetnam, T.W. 2009. Ecological foundations for fire management in North American forest and shrubland ecosystems. USDA Forest Service General Technical Report PNW-GTR-779: 1-92.
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 3

of variability. Since 1955, the medical sciences have relied instead on an alternative approach to research synthesis using the techniques of meta-analysis (Stroup 

and others 2000).

Meta-analysis is a systematic and quantitative approach to research synthesis that 

combines and compares results from independent trials to assess the direction, 

magnitude, and consistency of reported responses (Cooper and others 2009). 

Meta-analysis is now commonly applied to ecological questions (Gurevitch 

and others 2001) and has been recently applied to the wildland fuels treatment 

literature, as well (Martinson 1998, Wan and others 2001, Kopper 2002, Boerner 

and others 2009, Kalies and others 2010, Youngblood 2010). Kopper and others 

(2002, 2009) conducted a meta-analysis on the effects of prescribed fire on fuel 

reduction. The focus of the current meta-analysis is on the literature documenting 

fuel treatment performance in mitigating subsequent fire intensity and severity 

to assess the empirical support for the current fuel management paradigm.

Methods ________________________________________

Meta-analysis involves a comprehensive literature search for relevant studies, 

quantification of the magnitude of effects reported in the studies selected for 

inclusion, and an analysis of study heterogeneity to identify the strength and 

significance of any emergent trends. Robust methods for extracting and analyzing 

data embedded in disparate studies contribute to the strength of meta-analytic 

investigations.

Literature Search

Our literature search employed eight documents (table 1) as sources to identify the scientific publications relevant to our meta-analysis. These sources are 

traditional literature reviews that address fuel management issues and all references cited in them were subjected to an initial screening, as was any subsequent 

document published prior to September 1, 2009, that included at least one of the 

reviews in its literature cited (as identified by Google Scholar [http://scholar.

google.com]). Final reports submitted to the Joint Fire Science Program (http://

www.firescience.gov) prior to June 1, 2010, were also screened for relevant data 

and additional publications.

Study Selection

The initial screening broadly categorized all identified publications (n = 1213) 

based on the characteristics of the fuel treatments investigated (actual treatment 

[n=280], hypothetical or planned treatment [n=97], or no fuel treatment [n=836]) 

and the response variable analyzed (actual fire characteristic [n = 105], simulated 

fire characteristic [n=161], or no fire characteristic reported as a response variable 

[n = 947]). All publications that included information from actual fuel treatments 

exposed to actual fire (n = 60) were thoroughly reviewed and coded with respect 

to the following information: study design, location, dominant vegetation, fuel 
4 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

and fire variables measured, and treatment characteristics reported (AppendixA). 

Publications that did not include both a fuel treatment and a subsequent fire 

test were outside the focus of our meta-analysis, while simulation studies were 

excluded from further consideration for being more theoretical than empirical 

and overly encumbered by inherent inconsistencies in investigator assumptions 

and model errors (Cruz and Alexander 2010).

Studies of fuel treatment performance generally employ one of three basic designs: 

pre-planned block experiments (n = 9) burned intentionally or serendipitously, 

retrospective paired comparisons of treated and untreated areas affected by 

wildfire (n = 18), and landscape or regional surveys of wildfire severity or area 

burned (n = 33). Most of the latter lacked any semblance of control for variations 

in weather and topography (n = 28) and were excluded from further analysis, 

though recent advances in the application of spatial statistics show promise of 

improved information from such studies in the future by accounting for autocorrelated influences on fire behavior (for example, Wimberly and others 2009).

A variety of response variables are reported in the fuel treatment performance 

literature, and many report more than one measure. Among the 32 publications 

that remained under consideration for our meta-analysis, reported response variables comprised flame length (n = 7), fire spread rate (n = 5), percent canopy 

consumption (n = 6), percent canopy scorch (n = 11), scorch height (n = 7), 

bole char height (n = 8), percent mortality (n = 8), categorical canopy damage 

rating (n = 10), categorical ground char rating (n = 9), remotely sensed severity 

(n = 4), and area burned (n = 2). Rather than restricting our analysis to a single 

response or performing separate meta-analyses for each, we used correlation 

and simple regression analysis to determine if any could be reasonably treated 

as substitutes for one another and combined in a single meta-analysis. This 

allowed inclusion of a broader range of vegetation and treatment types in our 

meta-analysis, but also may have introduced additional error, so we compared 

the results of our meta-analysis with and without incorporating response variable 

substitution. Publications that reported responses with metrics that could not be 

equated to others were reserved for potential separate meta-analyses, resulting 

in the exclusion of 13 additional publications from the primary meta-analysis 

and a remainder of 19.

Data Extraction

Central to any meta-analysis is the calculation of effect sizes: dimensionless 

measures of the magnitude of difference between treatment and control means 

reported in the selected publications. Hedge’s d (Hedges and Olkin 1985) is the 

conventional effect size metric for difference data (such as treatment-control 

comparisons), but the log response ratio (Hedges and others 1999) has been found 

to be a more flexible metric for meta-analysis of ecological and natural resource 

studies (Kopper and others 2009). The log response ratio is calculated as a treatment mean divided by a control mean with the ratio log transformed, and is the 
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 5

measure of effect size used in in the current application. Data on responses and 

explanatory variables were extracted and effect sizes were calculated for the 19 

publications selected for inclusion in a step-wise analysis of heterogeneity using 

MetaWin software (Rosenberg and others 2000).

Our primary hypothesis was that there would be broad differences in effects 

reported from different types of ecosystems, as defined by general categories 

of geographic location (Northwestern, Southwestern, and Eastern United States 

and outside of the continental United States) and vegetation (long-needle conifer 

forests, mixed conifer forests, other woodlands, and grasslands). We also hypothesized that fuel treatment effects would vary among different types of fuel 

treatment. Treatments were categorized for comparisons into six broad types 

based on expected change to canopy and surface fuels (table 2).

Many of the 19 studies selected for inclusion in the meta-analysis contained 

information from more than one treatment type, and these were considered independent observations such that a total of 62 were included; their distribution 

among vegetation, region, and treatment categories is summarized in table 2 and 

listed along with reported responses and calculated effect sizes in Appendix B. 

Any remaining variability in effect sizes within the ecosystem and treatment 

categories was explored for relationships to treatment age and treatment intensity, 

as indicated by changes to measured fuel conditions.

Table 2—Distribution of observations included in the meta-analysis among treatment 

type categories within vegetation and region groups.

Treatment typea

1 2 3 4 5 6 Total

Vegetation

Long-needle pine forest 6 12 1 1 0 3 23

Mixed conifer forest 9 6 1 0 1 13 30

Woodlands other than conifer forest 0 2 0 2 0 1 5

Grasslands 0 2 0 2 0 0 4

Total 15 22 2 5 1 17 62

Region

Northwest U.S. 9 6 2 1 1 7 26

Southwest U.S. 6 8 0 0 0 9 23

Eastern U.S. 0 3 0 4 0 1 8

Non-U.S. 0 5 0 0 0 0 5

a Treatment descriptions in order of expected effectiveness (most to least):

1. Canopy thinned with slash and surface fuels reduced by burning or mechanical removal.

2. Canopy untreated, but surface fuels reduced by burning, mechanical removal, or grazing/

browsing by livestock or other biological vectors.

3. Canopy thinned with no change to surface fuels via whole tree extraction.

4. Canopy untreated, but surface fuels rearranged by physical or chemical means (mastication, 

chipping, crushing, piling, herbicide application).

5. Canopy thinned with slash and surface fuels rearranged as above.

6. Canopy thinned with no treatment of the activity fuels added to the surface.
6 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

Data Analysis

Meta-analyses of classically designed laboratory experiments typically employ 

parametric fixed effects statistical models that weight each study by its sample 

size and variance. However, field studies in ecology (Adams and others 1997) 

and especially in wildland fire research (Van Mantgem and others 2001) are 

more often observational than experimental, rarely conform to parametric assumptions, and are generally pseudo-replicated (in the sense of Hurlbert 1984). 

Measures of variability are thus often misspecified or unreported and any conditional weighting scheme would seem capricious. Kopper and others (2009), 

for example, could find only eight studies that met the criteria for inclusion in 

a parametric fixed-effects meta-analysis of fuel reduction by prescribed fire in 

ponderosa pine, even though it is unlikely there is another fuel treatment topic 

more thoroughly studied.

Adams and others (1997) suggested that mixed-effects and/or non-parametric 

meta-analysis may be most appropriate for many ecological topics. A non-parametric meta-analysis that employs randomization procedures (that is, resampling 

and bootstrapping) to estimate within-group effect size distributions and tests of 

between-group heterogeneity is analogous to a mixed-effects model (Gurevitch 

and Hedges 1999). Given the limited number of studies that address our topic of 

interest and their general inability to strictly adhere to parametric assumptions, 

we erred on the side of inclusiveness in using an unweighted, non-parametric, 

mixed-effects model for this meta-analysis. Kalies and others (2010) similarly 

found the relaxed assumptions of this model formulation most useful for their 

meta-analysis of wildlife responses to fuel treatments in the Southwest. We 

also followed Adams and others’ (1997) recommendation of 4999 resampling 

iterations for significance tests and the use of bias-correction for bootstrapped 

confidence intervals.

Nonetheless, an unweighted meta-analysis is more prone to bias from small studies. We performed diagnostic checks for the potential influence of small study 

bias on our meta-analysis with funnel plots and correlation analysis of study size 

versus effect size (Rothstein and others 2005). However, statistical reporting is 

inconsistent across the publications synthesized in our meta-analysis, including 

the definition of a sampling unit. We therefore determined the apparent number 

of distinct treatment units sampled for each study as the measure of its relative 

size for purposes of our bias diagnostics.

Parametric meta-analyses proceed with study segregation and tests for betweengroup heterogeneity until within-group heterogeneity is no longer found to be 

significant or there are no additional grouping variables available. But no statistical 

test for within-group heterogeneity is available for non-parametric meta-analysis 

(Gurevitch and Hedges 1999). We instead relied on an information-theoretic approach (Burnham and Anderson 2002) to determine whether additional segregation within groups was warranted given the information available. Specifically, 
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 7

additional segregation of studies within groups was added only if it reduced 

model error when adjusted for degrees of freedom as calculated by Akaiki’s 

Information Criterion (AICc) corrected for small samples:

AAAAAAAA = nn ∗ llll QQ

nn

+ 2kk +

2kk kk + 1

nn − kk − 1 , [1]

where n is the number of observations, k is the number of parameters (that is, group 

means) estimated, and Q is the maximum likelihood value for the meta-analytic 

model (also known as the homogeneity statistic), calculated in an unweighted 

meta-analysis as the sum of the squared errors for observed study effects when 

estimated from group means (Rosenberg and others 2000).

Model selection proceeded in a step-wise fashion, initiating with the most informative fully categorized main effect. The best model was determined at each step 

by comparison of Akaiki weights (w) representing the probability that a given 

model is the most informative of those considered (Burnham and Anderson 2002):

ww! = exp − ∆! 2

exp − ∆! 2 !

!!!

, [2]

where Δi and Δr are differences between the AICc value for a given model and 

the minimum AICc value found in the set of models considered at each selection step, and M is the number of models considered at each selection step. The 

model with the greatest Akaiki weight in each selection step was retained as the 

null model for the next step until all available explanatory variables had been 

considered.

Results _________________________________________

Correlation Among Response Variables

Percent crown volume scorch was the most prevalent response variable reported 

in studies of actual fuel treatment performance when subjected to fire. Within 

studies that reported more than one response measure, strong correlations were 

found among effect sizes calculated from percent crown scorch, scorch height, and 

canopy damage ratings (table 3). All studies that reported responses in terms of 

Table 3—Cross-correlation matrix for effect sizes derived from studies that reported multiple response variables. 

Number of contrasts for each pair of variables is in parentheses, and significant (p<0.05) correlation 

coefficients are emboldened.

Response % scorch scorch ht char ht ratinga mortality intensity

%scorch 1

scorch ht 0.78 (34) 1

char ht 0.54 (32) 0.45 (30) 1

ratinga 0.78 (33) 0.78 (29) 0.69 (26) 1

mortality 0.56 (7) 0.80 (4) 0.40 (3) na (0) 1

intensity 0.98 (3) na (0) na (0) na (0) 0.99 (3) 1

aRating of canopy damage based on scorch and/or consumption.
8 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

one of these three variables were equated to a common measure of effect with the 

regression relationships shown in figure 2. However, effect sizes were calculated 

from percent crown volume scorch for most (77%) of the studies included in the 

meta-analysis. Effect sizes were calculated from canopy damage ratings for three 

studies from prescribed burns in northwestern long-needled pine forests and one 

study from a prescribed burn in an eastern long-needled pine forest. Effect sizes 

were calculated from scorch height for the one study of surface fuel rearrangement in a long-needled pine forest, as they were for all studies in woodlands and 

grasslands by conversion of flame length measurements on experimental fires 

to potential scorch heights using relationships developed by Byram (1959) and 

Van Wagner (1973). The influence of these effect size substitutions on the final 

meta-analytic model is subsequently discussed.

Figure 2—Regression relationships to equate effect sizes calculated from (a) scorch height 

and (b) canopy damage ratings to effect sizes calculated from percent canopy scorch.

Distribution of Fuel Treatment Effect Sizes

Fuel treatment effect sizes varied widely among the studies included in the 

meta-analysis (figure 3), but the overall mean is large (-0.90) and significant 

with a 95% confidence interval that does not include zero (-1.32 to -0.56). Effect sizes ranged from -6.72 to 2.89 with 14 observations (22.5% of the total) 

demonstrating a negligible treatment effect, defined by convention as an absolute 

effect size less than 0.20 (Cohen 1988). Observations of non-negligible effects 

were mostly in the intended direction (43 of 48 [90%]) and these tended to be 

large (22 of 43 effect sizes <-0.8). Just one observation demonstrated a large 

counter-productive treatment effect, that is, a positive effect size greater than 

0.8, indicating more extreme fire behavior in the treated areas as compared to 

surrounding untreated areas. Paired photographs representing a range of effect 

sizes corresponding to those presented here are shown in figure 4, and additional 

examples from treatments sampled by Omi and Martinson (2002) and Omi and 

others (2006) are available online (http://omiassociates.net/fueltreatment/) along 

with a database selection tool.
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 9

Figure 3—Distribution of fuel treatment effect sizes calculated from the 19 publications included in the meta-analysis, 

distinguished by the characteristics found to be most informative from the model selection process. Legend abbreviations are: cone = conifer forest, wood = woodland other than conifer forest, grass = grassland, C = canopy thin, 

HC = heavy thin, LC = light thin, Rx = recent surface fuel reduction, and S = surface fuel treatment other than recent 

reduction. Negative effect sizes indicate lower fire intensity/severity in areas that received fuel treatment. Effect sizes 

are arranged from top to bottom in order of increasing absolute difference from zero, or no effect. Additional details 

and literature citations for the referenced data sources are provided in the appendices.
10 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 Figure 4—Pictorial representation of a range of effect sizes corresponding to those used to standardize study findings for the current meta-analysis. Photos in the top row are of treated areas adjacent and topographically similar to the untreated areas pictured in the bottom row with both affected by wildfire in the same burning period (Omi and Martinson 2002, Omi and others 2006): (a) mechanical thin with slash yarded and burned in 2001 within the 2003 Davis Fire in Oregon (b) mechanical thin with slash piled and burned in 2002 within the 2004 Fischer Fire in Washington (c) 1994 underburn within the 2004 Power Fire in California (d) blowdown fuels yarded and burned in 1997 within the 1999 Megram Fire in California (e) mechanical thin with no slash treatment within the 2000 Cerro Grande Fire in New Mexico (f) mechanical thin with no slash treatment within the 2003 Aspen Fire in Arizona
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 11

Model Selection

The goal of our meta-analysis was to identify the most informative organizing 

characteristics of fuel treatment studies to explain the variability in reported findings. Study characteristics considered were ecosystem and treatment conditions 

defined in terms of: vegetation, geographic region, treatment type, treatment 

age, and treatment intensity.

Vegetation Type

Most studies of fuel treatment effectiveness that met our study selection criteria 

were conducted in conifer forests (85%), with nearly half of those in longneedle pine systems (table 2). Just five studies were identified from grassland 

ecosystems and only four from woodlands other than conifer forests. Initial 

model selection suggested that the most informative study segregation was by 

these vegetation types (w>0.99, relative to a maximum possible value of 1.0) 

when compared to grouping studies only by geographic region or treatment 

type (table 4, step 1). Three study grouping schemes by vegetation type were 

then considered: separating grasslands from non-grasslands (table 4, step 2.a), 

further separating non-grasslands into conifer forests and other woodlands (table 

4, step 2.b), and further separating long-needled pine from other conifer forests 

(table 4, step 2.c). The model with three vegetation groups (grassland, conifer 

forest, and other woodlands) had the most support in the data (w = 0.54), while 

the model that further distinguished long-needled-pine from other conifer forests 

had the least support (w = 0.17).

Geographic Region

The fuel treatment studies included in our synthesis were concentrated in the 

western United States (79%), with these divided roughly evenly between northwest 

and southwest (distinguished approximately by the 40th parallel). Eight studies 

were identified from east of the Rocky Mountains and five from outside of the 

continental United States (Portugal and Australia). We next assessed whether there 

was support in the data for adding these regional groupings within the selected 

vegetation groupings for grasslands, conifer forests, and other woodlands. We 

first separated studies conducted in the continental United States from those that 

were not (table 4, step 3.a), then separated the western United States from the 

eastern (step 3.b), and finally northwest from southwest (step 3.c). However, the 

data did not support a more refined separation of studies than simply continental 

United States versus non-United States (w = 0.40), and the second best model for 

a regional effect was the null (table 4, step 3.0). Treatments in conifer forests appear to perform better in the United States (lower 95% confidence interval<mean 

effect size<upper 95% confidence interval = -1.25<-0.85<-0.56) than elsewhere 

(-0.11<-0.06<0.00), while treatments in woodlands performed better outside 

the United States (-1.70<-1.02<-0.34) than within (0.27<1.23<2.89). However, 

any regional influence on treatment effectiveness proved uninformative upon 

inclusion of treatment type and age as explanatory variables (table 4, step 5.c).
12 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

Table 4—Selection of most informative meta-analytic model for fuel treatment effect sizes: variables included (Model), the homogeneity statistic (Q), number of estimated parameters (k), Akaiki’s Information Criterion (AICc), and probability (w) of 

each model being the most informative of those considered at each step in the selection process. The model selected 

from each step is emboldened and italicized and was included as the null for the subsequent step.

Model Q k AICc w

Step 1: Most informative main effect

null 140.07 1 52.60 0.00

Region (R) 133.93 4 56.45 0.00

Treatment (T) 123.79 6 56.40 0.00

Vegetation (V) 100.42 4 38.60 1.00

Step 2: Vegetation categories

a) V(g = grassland) x V(w+c = non-grassland) 106.28 2 37.62 0.28

b) V(g) x V(w = woodland) x V(c = conifer forest) 100.44 3 36.32 0.54

c) V(g) x V(w) x V(cm = mixed conifer) x V(cp = long-needle pine) 100.42 4 38.60 0.17

Step 3: Geographic categories

0) V* = V(g) x V(w) x V(c) x R( null) 100.44 3 36.32 0.33

a) V* x R(US = United States) x R(xUS = non-US) 92.64 5 35.97 0.40

b) V* x R(xUS) x R(USw = west of Rocky Mountains) x R(USe = eastern US) 91.02 6 37.33 0.20

c) V* x R(xUS) x R(USe) x R(USnw = north of 40th parallel) x R(USsw = Southwest) 90.68 7 39.65 0.06

Step 4: Treatment types

0) V* x R* = R(US) x R(xUS) x T(null) 92.64 5 35.97 0.02

a) V* x R* x T(c = canopy thin) x T(no c) 92.24 6 38.16 0.01

b) V* x R* x T(s = surface treatment) x T(no s) 86.56 6 34.90 0.03

c) V* x R* x T(c&s) x T(c or s) 83.93 6 32.30 0.10

d) V* x R* x T(c) x T(s) x T(c&s) 82.58 7 34.59 0.03

e) V* x R* x T(c) x T(s- = reduce) x T(c&s-) x T(so = other surface treatment) 71.33 8 28.31 0.73

f) V* x R* x T(c) x T(s+) x T(s-) x T(c&so) x T(c&s-) 71.77 9 32.35 0.10

Step 5: Treatment age

0) V* x R* x T* = T(c) x T(so) x T(s-) x T(c&s-) x age(null) 71.33 8 28.31 0.07

a) V* x R* x T* x T(10s- = recent surface fuel reduction) 66.92 9 27.18 0.13

b) V* x R* x T* x T(10s-) x T(10c = recent thin) 66.93 9 27.19 0.13

c) V* x T* X T(10s-) 67.88 8 24.34 0.52

d) V* x T* X T(10s-) x T(c&so) 67.69 9 26.90 0.15

e) V* x T* x T(ages- = age of surface reduction) 74.46 9 32.81 0.01

Step 6: Treatment intensity

0) V* x T* = T(c) x T(so) x T(10s-) x T(c&10s-) x Ti

 = treatment intensity(null) 67.88 8 24.34 0.00

a) V* x T* x Ti

(c&10s-) 40.61 9 -3.28 0.00

b) V* x T* x Ti

 (10s-) x Ti (c&10 s-) 43.30 10 9.11 0.00

c) V* x T* x T(ch=heavy thin&10s-) 40.64 8 -7.47 0.00

d) V* x T* x Ti

 (ch&10s-) 31.42 9 -20.69 0.11

e) V* x T* x Ti

(c) x Ti (ch&10s-) 31.09 10 -4.70 0.00

f) V* x T(10s-) x Ti

 (ch&10s-) x T(-10s- = treated w/o recent surface reduction) 30.73 8 -24.80 0.89
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 13

Treatment Type

Different treatment types varied in prevalence among vegetation types and geographic regions (table 2). Reduction of natural fuels (treatment type 2), primarily 

by underburning, was the most common treatment overall (35%), but concentrated in long-needle pine forests (55%), while fairly evenly distributed across all 

regions. Canopy thinning without subsequent treatment of activity fuels (type 6) 

was the next most common treatment (27%), but concentrated in mixed conifer 

forests (74%) and roughly evenly divided between the northwest and southwest 

regions of the continental United States. Canopy thinning followed by reduction 

of activity and natural surface fuels (type 1) was somewhat less common overall 

(24%) but more prevalent in the northwest region (60%) and in mixed conifer 

forests (60%). The remaining 14% of the observations included in our analysis 

were scattered scantily among the other treatment types and across vegetation 

and region categories, though it is notable that rearrangement of natural fuels 

(type 4) was concentrated in non-conifer systems east of the Rocky Mountains.

Given the grouping of studies among the three vegetation types in U.S. ecosystems versus non-U.S. ecosystems, we next explored further distinguishing 

observations of treatment performance by treatment type (table 4, step 4). We 

first considered the simplest models of treatment type by separating studies 

into two groups based on the fuel strata treated (steps 4a-c): (a) canopy (table 

2 treatment types 1, 3, 5, and 6 versus types 2 and 4), (b) surface (types 1, 2, 4, 

and 5 versus types 3 and 6), and (c) canopy plus surface (types 1 and 5 versus 

types 2, 3, 4, and 6). Next, we grouped studies into three treatment categories 

(table 4, step 4.d): canopy plus surface treatment (table 2, types 1 and 5), canopy 

treatment-only (types 3 and 6), and surface treatment-only (types 2 and 4). Finally, we distinguished between surface treatments that result in fuel reduction 

(types 1 and 2) versus rearrangement (types 4 and 5). The most informative 

model (w = 0.73) included four treatment groups (table 4, step 4.e): canopy thin 

with surface fuel reduction (type 1), surface fuel reduction-only (type 2), canopy 

thin without surface fuel reduction regardless of whether the slash was otherwise 

rearranged (types 3, 5, and 6), and surface rearrangement-only (type 4). There 

are currently not enough studies to support a separation of whole-tree extraction 

from slash rearrangement or simply letting it lay (table 4, step 4.f: w = 0.10). 

The one study of thinning-only in a deciduous forest was grouped with the two 

studies of surface fuel rearrangements in that vegetation type, as the effect sizes 

were similar (0.80 versus 0.00<1.45<2.89).

Treatment Age

The median age of the treatments included in our synthesis was 3 years old 

when tested by fire, with a maximum of 20 years old in conifer forests, 8 years 

old in other woodlands, and 2 years old in grasslands. Treatments involving 

rearrangement of surface fuels were the most recent (median age of less than 

1 year), followed by surface fuel reduction treatments (median age of 2.5 years). 
14 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

Canopy-only treatments were generally oldest when affected by fire with a median age of 5 years.

We considered several variations of treatment age to further explain remaining 

variability within conifer forests, including surface reduction treatments categorized as recent (less than 10 years) or old (table 4, step 5.a), both surface reduction 

treatments and canopy treatments categorized as recent or old (table 4, step 5.b), 

and age of surface reduction as a continuous predictor of treatment effect size 

(table 4, step 5.e). The separation of old and new at approximately 10 years was 

apparent from visual inspection of the scatter plot of effect size versus treatment 

age (figure 5) and was the break point that minimized model error and AICc.

The most informative treatment age model distinguished recent surface reduction 

treatments in conifer forests from those older than 10 years, with older surface 

reduction treatments then grouped with surface rearrangement treatments (table 

4, step 5.a). The model was further improved by removing the regional distinction 

between U.S. and non-U.S. studies (table 4, step 5.c: w = 0.52). Distinguishing 

slash treatments by any method other than recent reduction remained unsupported 

given the available data (table 4, step 5.d), thus treatment types 3, 5, and 6 from 

table 2 were grouped together along with those treatments of type 1 that were 

more than 10 years old. Treatment age was not explored as a predictor of effect 

size in grasslands and woodlands due to the paucity of available studies within 

Figure 5—Fuel treatment effect size versus treatment age distinguished 

by treatment type among studies conducted in coniferous forests.
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 15

treatment categories and the limited range of treatment ages that have been 

investigated in these vegetation types. Mean effect sizes with 95% confidence 

intervals for the three vegetation groups and separated by the selected treatment 

type and age categories are displayed in figure 6 for coniferous forests and in 

figure 7 for grasslands and woodlands.

Figure 6—Fuel treatment effect sizes (mean and 95% confidence 

interval) among studies conducted in coniferous forests and 

grouped by (1) thinning followed by recent (<10 years prior to wildfire) surface fuel reduction, (2) recent surface fuel reduction with 

no canopy treatment,( 3) surface fuel treatment excluding recent 

reduction, and (4) thinning without recent surface fuel reduction.

Figure 7—Fuel treatment effect sizes (mean and 95% confidence 

interval) among studies conducted in ecosystems other than 

coniferous forests and grouped by (1) surface fuel reduction in 

grasslands, (2) surface fuel rearrangement in grasslands, (3) surface 

fuel reduction in woodlands, and (4) surface fuel rearrangement in 

woodlands (including thin-only).
16 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

Treatment Intensity

Finally, we investigated whether our meta-analytic model would be improved 

by including a measure of treatment intensity. We considered four measures of 

treatment intensity for studies conducted in conifer forests: treatment effect size 

(log response ratio) on residual tree diameter, effect size on height to canopy, 

effect size on canopy bulk density, and a composite measure of treatment intensity calculated as the average of treatment effect sizes on all of the above (with 

the effect on canopy bulk density inverted). As a point of reference, untreated 

stands had an average tree diameter of 25.3 cm, an average height to canopy 

of 4.7 m, and an average canopy bulk density of 0.11 kg/m3. Though treatment 

effects on surface fuels likely would be an informative measure of treatment 

intensity as well, pre-wildfire loadings generally cannot be reconstructed in 

retrospective studies (Martinson and Omi 2008) and were not reported with 

enough consistency to include in our synthesis (n<5 for all treatment categories).

Recent surface reduction treatments preceded by canopy thinning produced the 

greatest change in all measures of treatment intensity (MES = 0.51, 0.73, and 

-0.93 for residual tree diameter, height to canopy, and canopy bulk density, respectively, with all 95% confidence intervals excluding zero). The best predictor 

of this treatment type’s effect on crown scorch metrics of wildfire severity was the 

composite that combined all three measures (r2 = 0.62, versus 0.43 for residual 

diameter, 0.36 for canopy bulk density, and 0.26 for height to canopy). Thinning 

followed by surface fuel reduction on average increased mean tree diameter by 

16.8 cm (range in percent change = -11% to 228%), raised height to canopy by 

5.0 m (range in percent change = 0% to 267%), and reduced canopy bulk density 

by 0.07 kg/m3 (range in percent change = -78% to 6%).

Recent surface reduction without canopy treatment was found to increase both 

residual tree diameters (mean effect size [MES] = 0.22) and height to live canopy 

(MES = 0.42) by significant amounts (95% confidence interval does not include 

zero), though the differences in tree diameters had the strongest relationship to 

differences in wildfire severity for this treatment type (r2 = 0.28 versus r2<0.04 

for all other measures of treatment intensity). Recent surface reduction treatments on average effectively increased mean tree diameter by 6.2 cm (range in 

percent change = -15% to 95%) and height to canopy by 2.5 m (range in percent 

change = -11% to 828%).

Canopy treatments not followed by recent surface reduction significantly increased 

residual tree diameters (MES = 0.22) and decreased canopy bulk density (MES 

= -0.74), but no measure of treatment intensity demonstrated a relationship to 

wildfire severity for this treatment type (all r2<0.06 with canopy bulk density 

marginally strongest). These treatments on average increased mean tree diameter 

by 6.2 cm (range in percent change = -11% to 112%) and reduced canopy bulk 

density by 0.06 kg/m3 (range in percent change = -93% to 70%).
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 17

Surface treatments other than recent fuel reduction and not preceded by canopy 

thinning had no significant effect on any measure of treatment intensity, and 

these were not assessed for a relationship to wildfire severity due to the small 

number of available studies in this treatment category (n = 3 that included treatment intensity data).

The best measure of treatment intensity for each treatment type was then considered for inclusion in the overall meta-analytic model (table 4, steps 6.a-b, e). 

Visual inspection of the scatter plots of treatment intensity effect sizes versus fire 

severity effect sizes also suggested a threshold effect for the thinning followed 

by surface reduction treatment type, such that studies with a composite intensity 

value less than 0.80 (a change to a less hazardous condition of approximately 

125%, as measured by the combination of tree diameter, height to canopy, and 

canopy bulk density) appear to perform more like surface reduction-only treatments (figure 8).

Figure 8—Scatter plots for studies conducted in coniferous forests that included recent surface reduction 

treatments showing fuel treatment effect sizes on fire severity versus two measures of treatment intensity; 

(a) effect size on residual tree diameter and (b) a composite of effect sizes on residual tree diameter (DBH), 

height to canopy (CBH), and inverted canopy bulk density (-CBD). Surface reduction treatments are distinguished by whether they were preceded by “heavy” thinning (composite measure of intensity≥0.8), “light” 

thinning (composite intensity<0.8), or no thinning.
18 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

The most informative treatment intensity models distinguished “heavy” thinning 

from “light” thinning (table 4, step 6.c), grouped light thinning with no thinning 

when followed by recent surface fuel reduction, and included the composite 

indicator of treatment intensity as a continuous predictor of the effectiveness of 

heavy thinning treatments that were followed by recent surface fuel reduction 

(table 4, step 6.d). The final model selected (table 4, step 6.f: w = 0.89) also 

grouped thinning treatments not followed by recent surface fuel reduction with 

surface treatments in conifer forests other than those involving recent reduction 

(that is, rearrangement or reduction treatments more than 10 years old). While 

there appears to be some relationship between the effect of recent surface reduction 

on residual tree diameter and subsequent fire severity (figure 8.a), it is not strong 

enough to support inclusion in the overall model (table 4, step 6.b: w = 0.00).

Final Model Parameterization

The last model displayed at the bottom of table 4 was the final model selected 

for our meta-analysis of the literature on fuel treatment effectiveness; it is highly 

significant (p < 0.001) and explains 78% of the variability in reported findings 

(figure 9). The parameterized final model may be expressed as:

ŷ = ‐0.36 ‐ 0.32T(10s‐) + 6.11T(ch+10s‐) ‐ 9.43T(ch+10s‐) * Ti 

      + 1.59V(w) – 1.93V(w) * T(10s‐) – 2.07V(g) – 2.24V(g) * T(10s‐),

[3]

where ŷ is the estimated fire severity effect size expressed as the natural log of 

the ratio of percent crown volume scorch in a treated area to crown scorch in 

an adjacent and topographically similar untreated area; T(10s-) is an indicator 

variable equal to 1 for surface fuel reduction treatments completed less than 10 

years prior to a wildfire; T(ch+10s-) is an indicator variable equal to 1 for surface 

fuel reduction treatments completed less than 10 years prior to a wildfire and 

preceded by heavy thinning such that Ti is at least 0.8; Ti is a measure of thinning intensity calculated as the average of the treatment effect sizes on mean 

residual tree diameter, height to canopy, and canopy bulk density with the latter 

multiplied by -1; V(w) is an indicator variable equal to 1 for woodlands other 

than conifer forests; and V(g) is an indicator variable equal to 1 for grasslands. 

The intercept value of -0.36 thus represents the expected effect size when all 

indicator variables are equal to zero, that is, any treatment in a conifer forest that 

does not include recent surface fuel reduction.

Effect size estimates for fire behavior observations in woodlands and grasslands 

are scaled for comparison purposes to the effect size estimates calculated from 

fire severity measurements in coniferous forests. Observed effect sizes from 

flame length measurements would be approximately half as large as estimates 

predicted by the meta-analytic model within the detectable range of crown volume 

scorch measurements. Had we restricted the meta-analysis to only those studies 
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 19

that reported responses in terms of crown volume scorch, then woodlands and 

grasslands would be excluded from the model, the mean effect size estimate for 

recent surface fuel reduction treatments in conifer forests would be 15% larger 

with its 95% confidence interval 23% wider, and the estimate for any treatment 

in a coniferous forest that does not include recent surface fuel reduction would 

be 7% smaller with its 95% confidence interval 5% wider.

Diagnostics for Bias

An unweighted meta-analysis such as that presented here may be unduly influenced by large and inaccurate effect sizes produced by small studies. Any 

research synthesis is also vulnerable to publication bias—a tendency for statistically non-significant results or unexpected findings from small studies to be 

omitted from the published literature. The funnel plot for all studies included in 

our meta-analysis (figure 10a) suggests possible bias from small studies; small 

study effect sizes are larger and more variable about the overall mean than are 

effect sizes from larger studies. The plot is also asymmetrical with an apparent 

omission of small studies that demonstrate counter-productive treatment effects.

Figure 9—Predicted versus observed fuel treatment effect sizes on fire severity (negative 

values indicate lower severity in a treated area). Legend abbreviations are: cone = conifer 

forest, wood = woodland other than conifer forest, grass = grassland, C = canopy thin, HC = 

heavy thin, LC = light thin, Rx = recent surface fuel reduction, and S = surface fuel treatment 

other than recent reduction.
20 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

However, funnel plots may be misleading when there is real heterogeneity 

among study categories (Lau and others 2006). Evidence of small study bias 

largely disappears when studies are segregated by the categories selected in our 

final meta-analytic model (figures 10.b-d). Significant small study bias would 

be indicated by a strong correlation either between study size and effect size 

or between study size and deviation from the group mean, but such correlation 

coefficients were found to be weak (r<0.33) and not significantly different from 

zero (p>0.15) for all study groups. We therefore conclude that our meta-analysis 

was not compromised by potential small study biases.

Figure 10—Funnel plots of study size versus effect size: (a) across all studies, (b) among studies in coniferous forests that included recent surface fuel reduction not preceded by heavy thinning, (c) among studies in 

coniferous forests treated by any means that did not include recent surface fuel reduction, and (d) among the 

less well-studied groups not included in (b) or (c). The asymmetrical form of (a) is suggestive of publication 

bias in the fuel treatment literature, but also appears to be explained by the study grouping selected for the final 

meta-analytical model. Legend abbreviations are: cone = conifer forest, wood = woodland other than conifer 

forest, grass = grassland, C = canopy thin, HC = heavy thin, LC = light thin, Rx = recent surface fuel reduction, 

and S = surface fuel treatment other than recent reduction.
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 21

Discussion ______________________________________

Previous reviews of the literature on fuel treatment effectiveness have noted 

above all a paucity of empirical data and heavy reliance on anecdote, theory, and 

modeling (Carey and Schumann 2003, Martinson and Omi 2003, Graham and 

others 2004, Peterson and others 2005). Martinson and Omi (2003) abandoned an 

initial attempt to conduct a meta-analysis on this topic due to the lack of comparable quantitative information. But the literature on fuel treatment effectiveness 

has expanded considerably in the last few years and the number of publications 

(19) we were able to include in this meta-analysis is comparable to others that 

have recently been conducted on fuel treatment topics (22 in Kalies and others’ 

[2010] meta-analysis of wildlife responses, 12 in Boerner and others’ [2009] 

meta-analysis of effects on soil properties, 8 in Kopper and others’ [2009] metaanalysis of effects on fuel loads, and 7 in Youngblood’s [2010] meta-analysis of 

effects on diameter distributions). Our synthesis of fuel treatment effectiveness 

studies highlights several considerations that both support and inform the current 

fuels management paradigm.

We found that the overall mean effect of fuel treatments on fire responses is large 

and significant, equating to a reduction in canopy volume scorch from 100% in 

an untreated stand to 40% in a treated stand, a reduction in scorch height from 

30.5 m to 16.1 m, or an inferred reduction in flame length from 3.4 m to 2.1 m. 

But our synthesis demonstrates that fuel treatments vary widely in effectiveness, 

which is largely explained by vegetation and treatment type.

Treatments have proved most effective in grasslands and in conifer forests that 

were heavily thinned and subsequently burned, while the least effective treatments have been mechanical rearrangements in woodlands. The extreme case 

of treatment effectiveness observed a reduction in crown volume scorch from 

83% in untreated mixed conifer forest to less than 1% in an adjacent stand that 

was thinned and burned one year previously (an effect size 7.5 times larger 

than the overall mean). The extreme case of treatment ineffectiveness reported 

an increase in flame length from 25 cm in untreated oak woodland to 74 cm in 

adjacent fuels treated by mechanical mastication (an effect size 3.2 times greater 

than the mean and in the opposite direction).

The relative effectiveness of treatments in grasslands, conifer forests, and 

woodlands is as would be expected from the hypothesis that treatments will be 

most effective where available fuel accumulates most rapidly and where fire 

was historically most frequent, based on coarse fire regime constructs (such as 

Schmidt and others 2002). However, we were surprised to find no differences in 

fuel treatment effectiveness between long-needle pine and mixed conifer forests 

or between the northern and southern latitudes of the western United States. This 

suggests that fuel treatment effectiveness may be less sensitive to climatic gradients in western forests than has been proposed in previous reviews (Schoennagel 

and others 2004), though it should be noted that none of the studies included in 
22 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

our synthesis extended into the upper elevations or latitudes dominated by shortneedle conifers where fire was historically least frequent. One anecdote from 

such systems indicates that thinning may exacerbate fire behavior (Alexander 

and Lanoville 2004), but data have not been presented that could be included in 

our synthesis.

That no relationship (r2<0.06) was found between canopy fuel variables and the 

effectiveness of either surface reduction treatments without thinning or thinning 

treatments without subsequent slash treatment supports the assertion that surface 

fuel reduction is of primary importance in influencing treatment effectiveness. 

Much of the variability within these treatment types would likely be explained 

by the amount of change in surface fuels that was actually produced, but surface 

fuels information was not reported with enough consistency to include in our 

synthesis.

However, it is notable that more often than not, thin-only treatments have been 

found to moderate fire responses in spite of the addition of slash fuels to the 

surface, though to a lesser degree than surface reduction treatments with or 

without prior thinning. Just 5 of the 18 studies of untreated slash (including the 

one where slash was left in piles) reported a counter-productive treatment effect, 

as compared to 1 of 3 studies of masticated fuels, 1 of 3 studies of underburns 

more than 10 years old, and 1 of 17 studies of more recent surface fuel reduction 

treatments (figure 3). The effectiveness of thin-only treatments likely depends 

on whether fire enters the treated stand as an active crown fire or as a surface 

fire, as the additional surface fuels increase the likelihood of torching, but the 

more open canopy reduces the likelihood of sustained crown fire (Scott and 

Reinhardt 2001).

The best available predictor of the effectiveness of surface reduction treatments 

was residual tree diameter. This variable was also included along with canopy 

variables as a predictor of the effectiveness of treatments that combined thinning 

and burning. Thus, Agee and Skinner’s (2005) recommendation to favor retaining large trees over small ones in order to improve the fire resistance of treated 

stands is supported. Thinning followed by surface fuel reduction was found to 

be the most effective type of treatment, as expected, but the added benefit of 

thinning appears to depend upon achieving a substantial change to canopy fuel 

conditions. A threshold was identified for the effectiveness of these combination 

treatments; those that achieved at least a 100% change to a less hazardous stand 

condition increased in effectiveness as fuel hazard decreased (as measured by 

the average change to mean tree diameter, height to canopy, and canopy bulk 

density). Lighter thinning treatments that reduced canopy fuel hazard by less than 

125% appear to perform no differently than surface fuel reduction treatments that 

did not include any mechanical thinning (figure 8). Based on average conditions 

in the represented untreated stands, the necessary thinning intensity to achieve 

any benefit beyond what would be produced by the surface treatment alone corresponds to an increase in mean tree diameter from 19 cm to 42 cm, an increase 
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 23

in height to canopy from 4 m to 9 m, and a decrease in canopy bulk density from 

0.09 kg/m3 to 0.04 kg/m3. We hasten to caution against any inference of thinning 

effectiveness beyond the data included in our meta-analysis and note that further 

reductions in canopy fuel hazard beyond 150% would be of marginal practical 

value even when followed by treatment of the slash, corresponding to a reduction 

in expected crown volume scorch in the treated area from 5% to something less. 

Also, no relationship was found between thinning intensity and subsequent fire 

response among the thin-only treatments, suggesting that any benefit from the 

reduction in canopy fuels is largely offset by the increase in surface fuels until 

they are reduced as well.

Management Implications

The results of this meta-analysis add empirical support for the basic principles 

of fuels management proposed by Agee and Skinner (2005) that emphasize the 

reduction of surface fuels and the preservation of the largest trees in a stand, 

while recognizing the importance of opening the canopy in order to achieve the 

maximum benefits of hazard reduction. This meta-analysis also confirms that 

all treatments may not be beneficial in all locations and provides a quantifiable 

estimate of the expected relative effectiveness of different types of treatment in 

broad vegetation categories. However, caution is warranted in extrapolating the 

results to ecosystems other than long-needle pine and mixed conifer forests due 

to the lack of empirical information on treatment effectiveness and the potential 

for negative ecological consequences, such as invasion by more flammable nonnative species (Martinson and others 2008).

But treatments that include surface fuel reduction, particularly by prescribed 

burning, are well supported for moderating potential wildfire behavior in both 

long-needle pine and mixed conifer forests. These treatments appear to remain 

effective for up to 10 years, though longevity should be expected to vary by 

ecosystem productivity. Where crown fire hazard has become so high as to preclude initial entry with prescribed fire, mechanical thinning may be a necessary 

precursor. Thinning treatments have demonstrated the greatest reductions in 

wildfire severity, but only by those treatments that produce substantial changes 

to canopy fuels, shift the diameter distribution towards larger trees, and are followed by broadcast burning or other means of removal. Until the residual activity 

fuels are disposed, they will largely offset much of the hazard reduction benefit 

achieved from opening the canopy. While follow-up slash treatment may be 

generally intended, untreated slash seems to be encountered by large wildfires 

with surprising frequency (table 2, treatment type 6).

Modifications in fire behavior achieved within a single treated stand, however 

significant, are unlikely to change the total area burned by a large wildfire, aid 

fire control efforts, or impact the distribution of severities across a landscape 

(Finney and others 2003). Fuel treatment effectiveness ultimately depends on 

the cumulative impact of a treatment regime applied across landscapes and 
24 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

maintained through time. Optimization and assessment of treatment regimes 

rely on models that presume treatments will perform as expected (Finney and 

others 2007). Empirical fuel treatment performance studies, such as those included in this meta-analysis, help define the conditions under which theoretical 

expectations are met. Records of treatment boundaries, prescriptions, and fuel 

conditions are therefore critical components of fuel treatment implementation 

to enable effective adaptive management.

Recommendations for Future Research

Wildfires provide the best test of treatment performance under extreme conditions, 

but information from retrospective studies is limited to that provided by chance 

encounters. Such encounters are most likely where treatments and wildfires are 

most common, thus information is unevenly distributed among ecosystems, 

geographic locations, treatment types, and treatment ages (table 2; figure 10). 

Our search for studies to include in this synthesis highlights the need for greater 

attention to identifying treatments encountered by wildfires in all areas other than 

long-needle pine and mixed conifer forests west of the Rocky Mountains. Also, 

alternatives to prescribed fire for treating surface fuels have so far received little 

evaluation in any ecosystem from a fuel hazard perspective. Few of the studies 

included in our synthesis documented more than a single treatment entry other 

than follow-up slash treatments, and the relative effectiveness of initial entry 

treatments versus treatments that have been maintained at varying frequencies is 

in need of investigation as opportunities arise. The influence of treatment scale 

on modifying fire behavior both within treatments and beyond them is another 

consideration that has received little empirical evaluation.

Retrospective wildfire investigations are also limited by their maximum detectable response, which decreases with the height of the dominant vegetation, as 

well as their capacity to connect treatment effectiveness to the altered condition 

of any fuels the wildfire consumes. Despite the large overall mean and wide 

variability in fuel treatment effect sizes demonstrated by our meta-analysis, these 

were likely small relative to what might have been produced had the recording 

vegetation been taller or had fire behavior been measured directly. The addition 

of percent crown volume consumed to percent scorch estimates may provide a 

closer approximation to the effect of fuel treatments on modifying fire behavior, 

but has so far not been reported in the literature with enough frequency to include 

in our meta-analysis (Appendix A).

We suspect that fuel treatment effectiveness also depends on fire weather conditions, such that potential effect size is maximized somewhere between the 

extremes of low and high fire danger. Under very moderate conditions, fire 

behavior may be so benign regardless of fuelbed characteristics that there will 

be little detectable difference between treated and untreated areas. For example, 

negligible responses to treatments that included recent fuel reduction in our own 

investigations (Omi and Martinson 2002, Omi and others 2006) occurred on days 
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 25

when the Burning Index of the National Fire Danger Rating System was below 

the 80th percentile. At the other extreme, fire weather may overwhelm the influence of fuel manipulations, especially those applied at small scales (for example, 

Finney and others 2003). However, observations of fuel treatment performance 

have not been connected to fire weather conditions with enough consistency in 

the extant literature to be considered quantitatively in our meta-analysis. Future 

studies should fully report all available metrics of fire weather and fuel conditions, as well as treatment responses.

The focus of the literature included in this meta-analysis has been on indicators 

of fire behavior at the flaming front. But fuel treatment effectiveness also ultimately depends on long-term effects determined by the total heat release that is 

augmented by post-frontal combustion, including smoldering of large or deeply 

buried fuels (Neary and others 1999). Indicators of total heat release have been 

long proposed (Wells and others 1979) with some standardization attempted 

(Ryan and Noste 1985, Key and Benson 2006), but the ground component of 

fire severity measurement remains subjective, qualitative, and poorly connected 

to fire behavior, and thus needs further research attention.

An ideal evaluation of fuel treatment effectiveness would include measurement 

of all fuelbed components that contribute to flammability, would compare potential fire behavior in treated and untreated fuelbeds with predictive models, 

and would compare model predictions to observations from experimental fires 

or serendipitous wildfire events, with connection to post-fire evaluation of fire 

severity and repeated measurements of vegetation response over time. Direct 

measurement of fire behavior is the only comparable means to evaluate fuel treatment performance in non-forest ecosystems and is a worthy research endeavor 

in all, despite a high potential for failure to fully meet experimental objectives 

(Fites and Henson 2004).

Acknowledgments________________________________

This project was funded by the Joint Fire Science Program project 08-2-1-09. 

We thank L. Scott Baggett, Brandon Collins, Molly Hunter, and Jolie Pollet for 

thoughtful reviews that improved this manuscript and Paula Fornwalt for coordinating publication through the U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station.

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USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 29(continued) Appendix A—The 60 publications through May 2010 that contain empirical evidence of fuel treatment effectiveness, indicating study location(s), dominant vegetation type(s), treatment type(s), whether fuel conditions were measured, whether treatment age was reported, the response variable(s) measured, and whether control for weather and topography was demonstrated. The 19 publications that met all criteria for inclusion in the meta-analysis are emboldened. Treatment typea Response variablec Publication Location Vegetation 1 2 3 4 5 6 Fuelb Age 1 2 3 4 5 6 7 8 9 10 11 12 Control Moore NJ Forest/mixed/ and others 1955 pine-oak X X X X Weaver WA Forest/conifer/ 1957 mixed X X X Davis GA Forest/conifer/ and others 1963 pine X X X X Cumming NJ Forest/mixed/ 1964 pine-oak X X X X Van Wagner CA Forest/conifer/ 1968 pine X X X X X Kallander AZ Forest/conifer/ 1969 pine X X X Omi CA Forest & chaparral X X 1977 Wagle AZ Forest/conifer/ and others 1979 pine X X X X X Minnich CA/ 1983 Mexico Chaparral & scrub X X Martin SE US Unspecified X X X 1988 Wilson Australia Grassland X X X 1988 Dunn CA Chaparral X X X 1989 Omi ID Forest/conifer/ and others 1991 spruce-fir X X Buckley Australia Forest/deciduous/ 1992 eucalypt X X X Koehler FL Unspecified X X X 1992/93 Cheney Australia Grassland X X X X X and others 1993 Vihanek OR Forest/conifer X X X and others 1993
30 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 Weatherspoon CA Forest/conifer/ and others 1995 mixed X X X X X X Agee WA Forest/conifer/ 1996 fir X X X X Minnich CA/ Mexico Chaparral X X and others 1997 Hall MT Forest/conifer/ and others 1999 spruce-fir X X X X Keeley CA Chaparral X X X and others 1999 McCarthy Australia Forest X X X and others 2001 Omi NM/CA/ CO Forest/conifer/ and others 2002 mixed X X X X X X X X X X X X Pollet MT/WA/ and others 2002 CA/AZ Forest/conifer/ pine X X X X X X X X Prestemon FL Forest X X X X and others 2002 Wilmes AZ Forest/conifer/ and others 2002 pine X X X X Harma OR Undescribed X X X and others 2003 Martinson CO Forest/conifer/ and others 2003 pine X X X X X Moritz CA Chaparral X X 2003 Fernandes Portugal Forest/conifer/ and others 2004 pine X X X X X X X X X Kolaks MO Forest/mixed/ 2004 pine-oak X X X X X Oucalt FL/GA Forest/conifer/ and others 2004 pine X X X X Richburg MA/NY Forest/deciduous and others 2004 & shrub X X X X X X Stone MT Forest/conifer X X and others 2004 Finney AZ Forest/conifer/ and others 2005 pine X X X Keeley CA Chaparral & scrub X X X Appendix A and others 2005 —(Continued) Treatment typea Response variablec Publication Location Vegetation 1 2 3 4 5 6 Fuelb Age 1 2 3 4 5 6 7 8 9 10 11 12 Control (continued)
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 31 Appendix A—(Continued) Treatment typea Response variablec Publication Location Vegetation 1 2 3 4 5 6 Fuelb Age 1 2 3 4 5 6 7 8 9 10 11 12 Control Raymond OR Forest/conifer/ and others 2005 fir X X X X X X X X Bradley CA Woodland/mixed/ and others 2006 pine-oak X X X X X Cram AZ/NM Forest/conifer/ and others 2006 mixed X X X X X X X X X X X Glitzenstein SC Forest/conifer/ and others 2006 pine X X X X X X X X Omi CO/AZ/OR/ Forest/conifer/ and others 2006 CA/WA pine & mixed X X X X X X X X X X X X Schroeder NWT Forest/conifer/ 2006 mixed X X X X X Collins CA Forest/Conifer/ and others 2007 mixed X X Jain MT Forest/conifer/ and others 2007 pine X X X X X X Moghaddas CA Forest/conifer/ and others 2007 mixed X X X X Ritchie CA Forest/conifer/ and others 2007 pine X X X X X X X X Strom AZ Forest/conifer/ and others 2007 pine X X X X X Thompson OR Forest/conifer/ and others 2007 mixed X X X X Keeley CA Chaparral X X X and others 2008 Lezberg CO Forest/conifer/ and others 2008 pine X X X X Martinson MS Forest/conifer/ and others 2008 pine X X X X X X X X X McCaw Australia Forest/deciduous/ and others 2008 eucalypt X X X X X Safford CA Forest/conifer/ 2008 mixed X X X X X X X van Leeuwen AZ Forest/conifer/ 2008 pine X X X X Collins CA Forest/conifer/ and others 2009 mixed X X X Diamond NV Grassland X X X X X X X and others 2009 (continued)
32 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 Appendix A—(Continued) Treatment typea Response variablec Publication Location Vegetation 1 2 3 4 5 6 Fuelb Age 1 2 3 4 5 6 7 8 9 10 11 12 Control Safford CA Forest/conifer/ and others 2009 mixed X X X X X X X X X X X Wimberly AZ/MT/ WA Forest/conifer/ and others 2009 pine X X X X X Prichard WA Forest/conifer/ and others 2010 mixed X X X X X X a See table 2 for definitions of treatment types. b Indicator of whether any fuel conditions in both treated and untreated areas were measured pre-fire or reconstructed post-fire, such as surface fuel load and depth, tree density, basal area, tree diameters, height to canopy, and/or canopy bulk density. c Fuel treatment response variables are: 1. Flame length 2. Spread rate 3. Percent crown volume consumed 4. Percent crown volume scorched 5. Canopy damage rating based on scorch and/or consumption 6. Height of needle scorch 7. Height of bole char 8. Percent tree mortality 9. Depth of ground char rating 10. Remotely sensed fire severity rating 11. Binary crown fire indicator 12. Area burned
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 33

Appendix B—Summary of the datasets from the 19 publications included in the 

meta-analysis.

Treatment Response Effect

Source Location Vegetation typea age variableb Uc Td sizee

Moore NJ Forest/conifer/

and others 1955 pine 2 2 5 2.6 2.2 -0.3

Omi CA Forest/conifer/

and others 2002 mixed 2 2 4 71.4 16.9 -1.4

CO Forest/conifer/

 mixed 2 3 4 57.7 50.1 -0.1

CO Forest/conifer/

 mixed 6 8 4 88.4 72.3 -0.2

NM Forest/conifer/

 pine 1 5 4 89.8 53.8 -0.5

NM Forest/conifer/

 pine 6 2 4 99.6 75.7 -0.3

Pollet

and others 2002 WA Forest/conifer/

 pine 1 11 4 100.0 74.0 -0.3

AZ Forest/conifer/

 pine 2 1 4 99.0 29.0 -1.2

MT Forest/conifer/

 pine 2 5 4 67.0 26.0 -0.9

CA Forest/conifer/

 pine 3 4 4 78.0 26.0 -1.1

Fernandes

and others 2004 Portugal Forest/conifer/

 pine 2 2 4 100.0 88.0 -0.1

Portugal Forest/conifer/

 pine 2 3 4 100.0 94.0 -0.1

Portugal Forest/conifer/

 pine 2 13 4 100.0 100.0 0.0

Kolaks

2004 MO Forest/mixed/

 oak-pine 6 0 6 (1) 12.9 22.7 0.8

Richburg

and others 2004 MA Grassland 2 2 6 (1) 64.0 0.0 -6.1

MA Grassland 4 0 6 (1) 64.0 4.8 -3.7

MA Grassland 4 0 6 (1) 64.0 27.3 -1.2

MA Forest/deciduous 4 0 6 (1) 1.0 1.0 0.0

Raymond

and others 2005 OR Forest/conifer/

 mixed 1 1 4 83.0 0.1 -6.7

OR Forest/conifer/

 mixed 6 6 4 71.0 97.3 0.3

Bradley

and others 2006 CA Woodland/mixed/

 oak-pine 4 0 6 (1) 0.3 2.7 2.9

(continued)
34 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

Cram

and others 2006 AZ Forest/conifer/

 pine 1 3 4 100.0 45.9 -0.8

AZ Forest/conifer/

 pine 6 3 4 100.0 90.8 -0.1

NM Forest/conifer/

 mixed 1 7 4 97.2 15.9 -1.8

NM Forest/conifer/

 mixed 1 8 4 100.0 3.7 -3.3

NM Forest/conifer/

 mixed 6 10 4 99.2 71.5 -0.3

NM Forest/conifer/

 mixed 6 10 4 94.8 53.6 -0.6

NM Forest/conifer/

 mixed 6 14 4 100.0 59.4 -0.5

Glitzenstein

and others 2006 SC Forest/conifer/

 pine 4 0 6 2.5 1.3 -0.9

Omi

and others 2006 AZ Forest/conifer/

 pine 2 2 4 70.7 42.2 -0.5

AZ Forest/conifer/

 pine 2 7 4 52.8 21.0 -0.9

AZ Forest/conifer/

 mixed 6 2 4 75.0 59.7 -0.2

AZ Forest/conifer/

 mixed 6 7 4 61.3 77.2 0.2

CA Forest/conifer/

 mixed 2 9 4 82.5 25.9 -1.2

CA Forest/conifer/

 mixed 2 20 4 77.7 96.7 0.2

CA Forest/conifer/

 mixed 6 1 4 56.2 5.9 -2.3

CA Forest/conifer/

 mixed 6 5 4 88.1 49.0 -0.6

CO Forest/conifer/

 mixed 1 1 4 100.0 47.9 -0.7

CO Forest/conifer/

 mixed 1 10 4 75.6 79.4 0.0

CO Forest/conifer/

 mixed 2 1 4 81.0 20.1 -1.4

CO Forest/conifer/

 mixed 2 10 4 100.0 100.0 0.0

CO Forest/conifer/

 mixed 6 2 4 85.2 96.5 0.1

OR Forest/conifer/

 mixed 1 2 4 100.0 76.2 -0.3

OR Forest/conifer/

 mixed 6 0 4 99.7 97.7 0.0

OR Forest/conifer/

 mixed 6 2 4 88.9 84.1 -0.1

WA Forest/conifer/

 pine 1 2 4 55.5 44.4 -0.2

Appendix B—(Continued).

Treatment Response Effect

Source Location Vegetation typea age variableb Uc Td sizee

(continued)
USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013 35

Jain

and others 2007 MT Forest/conifer/

 pine 2 0 5 3.1 3.2 0.0

MT Forest/conifer/

 pine 2 2 5 2.5 2.1 -0.3

MT Forest/conifer/

 pine 2 5 5 2.9 2.3 -0.4

Ritchie

and others 2007 CA Forest/conifer/

 pine 1 4 4 86.0 1.0 -4.5

CA Forest/conifer/

 pine 1 5 4 99.3 10.0 -2.3

CA Forest/conifer/

 pine 6 6 4 66.7 22.0 -1.1

Martinson

and others 2008 MS Forest/conifer/

 pine 2 1 4 99.0 14.0 -2.0

McCaw

and others 2008 Australia Forest/deciduous/

 eucalypt 2 4 6 (1) 31.3 9.3 -1.7

Australia Forest/deciduous/

 eucalypt 2 8 6 (1) 31.3 24.6 -0.3

Safford

2008 CA Forest/conifer/

 mixed 3 12 4 93.8 52.5 -0.6

Diamond

and others 2009 NV Grassland 2 0 6 (1) 11.8 0.7 -3.9

Safford

and others 2009 CA Forest/conifer/

 mixed 1 2 4 94.6 57.0 -0.5

CA Forest/conifer/

 mixed 5 3 4 99.3 100.0 0.0

CA Forest/conifer/

 mixed 1 11 4 41.5 16.4 -0.9

Prichard

and others 2010 WA Forest/conifer/

 mixed 1 3 4 70.5 32.9 -0.8

WA Forest/conifer/

 mixed 6 12 4 70.5 70.5 0.0

a See table 2 for description of treatment types.

b See Appendix A for description of response variables. For five publications, scorch height (6) was estimated from reported flame length or 

fireline intensity measurements (1) using relationships developed by Van Wagner (1972) and Byram (1959).

c Mean response recorded in untreated areas.

d Mean response recorded in treated areas.

e Effect size calculated as ln (T/U), multiplied by 1.409 for scorch heights (6) or 1.779 for canopy damage ratings (5) to equate to percent crown 

volume scorch (4)—see figure 2.

Appendix B—(Continued).

Treatment Response Effect

Source Location Vegetation typea age variableb Uc Td sizee
36 USDA Forest Service Res. Pap. RMRS-RP-103WWW. 2013

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The Rocky Mountain Research Station develops scientific information 

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forests and rangelands. Research is designed to meet the needs of 

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