FrontiersinEcology

and the Environment

Can fuel-reduction treatments really

increase forest carbon storage in the

western US by reducing future fire

emissions?

John L Campbell, Mark E Harmon, and Stephen R Mitchell

Front Ecol Environ 2011; doi:10.1890/110057 

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Various levels of tree removal, often paired with prescribed burning, are a management tool commonly

used in fire-prone forests to reduce fuel quantity, fuel

continuity, and the associated risk of high-severity forest

fire. Collectively referred to as “fuel-reduction treatments”, such practices are increasingly used across semiarid forests of the western US, where a century of fire

suppression has allowed fuels to accumulate to levels

deemed unacceptably hazardous. The efficacy of fuelreduction treatments in temporarily reducing fire hazard

in forests is generally accepted (Agee and Skinner 2005;

Ager et al. 2007; Stephens et al. 2009a) and, depending

on the prescription, may serve additional management

objectives, including the restoration of native species

composition, protection from insect and pathogen outbreaks, and provision of wood products and associated

employment opportunities.

Recently, several authors have suggested that fuelreduction treatments are also consistent with efforts to

sequester C in forest biomass, thus reducing atmospheric carbon dioxide (CO2) levels (Frinkral and Evans

2008; Hurteau et al. 2008; Hurteau and North 2009;

Stephens et al. 2009b). It is argued that short-term

losses in forest biomass associated with fuel-reduction

treatments are more than made up for by the reduction

of future wildfire emissions, and thinning practices

aimed at reducing the probability of high-severity fire

should therefore be given incentives rather than be

penalized in C-accounting programs. This is an appealing notion that aligns the practice of forest thinning

with four of the most pressing environmental and societal concerns facing forest managers in this region

today – namely, fire hazard, economic stimulus, socalled forest health, and climate-change mitigation.

However, we believe that current claims that fuelreduction treatments function to increase forest C

sequestration are based on specific and sometimes unrealistic assumptions regarding treatment efficacy, wildfire emissions, and wildfire burn probability.

In this paper, we combine empirical data from various fire-prone, semiarid conifer forests of the western

US (where issues of wildfire and fuel management are

most relevant) with basic principles of forest growth,

mortality, decomposition, and combustion. Our goal is

to provide a complete picture of how fuel treatments

and wildfires affect aboveground forest C stocks by

examining these disturbance events (1) for a single forest patch, (2) across an entire forest landscape, (3)

after a single disturbance, and (4) over multiple disturbances. Finally, we consider how wildfire and/or fuel

treatments could initiate alternate equilibrium states

REVIEWS REVIEWS REVIEWS

Can fuel-reduction treatments really increase

forest carbon storage in the western US by

reducing future fire emissions?

John L Campbell1*, Mark E Harmon1, and Stephen R Mitchell2

It has been suggested that thinning trees and other fuel-reduction practices aimed at reducing the probability

of high-severity forest fire are consistent with efforts to keep carbon (C) sequestered in terrestrial pools, and

that such practices should therefore be rewarded rather than penalized in C-accounting schemes. By evaluating how fuel treatments, wildfire, and their interactions affect forest C stocks across a wide range of spatial

and temporal scales, we conclude that this is extremely unlikely. Our review reveals high C losses associated

with fuel treatment, only modest differences in the combustive losses associated with high-severity fire and

the low-severity fire that fuel treatment is meant to encourage, and a low likelihood that treated forests will be

exposed to fire. Although fuel-reduction treatments may be necessary to restore historical functionality to firesuppressed ecosystems, we found little credible evidence that such efforts have the added benefit of increasing

terrestrial C stocks.

Front Ecol Environ 2011; doi:10.1890/110057

In a nutshell:

• Carbon (C) losses incurred with fuel removal generally

exceed what is protected from combustion should the treated

area burn

• Even among fire-prone forests, one must treat about ten locations to influence future fire behavior in a single location

• Over multiple fire cycles, forests that burn less often store

more C than forests that burn more often

• Only when treatments change the equilibrium between

growth and mortality can they alter long-term C storage

1

Department of Forest Ecosystems and Society, Oregon State

University, Corvallis, OR *

(john.campbell@oregonstate.edu); 2Nicholas School of the Environment, Duke University, Durham, NC
Forest fuels, wildfire, and carbon JL Campbell et al.

www.frontiersinecology.org © The Ecological Society of America

and change the long-term capacity of a forest to accumulate biomass.

n Immediate stand-level C losses attributed to

wildfire and fuel-reduction treatments

Because fuel-reduction treatments are generally designed

to reduce subsequent wildfire severity, rather than to preclude fire entirely, it is important to compare the C losses

incurred under both high- and low-severity fire scenarios.

The amount of biomass combusted in a high-severity

crown fire is unquestionably greater than the amount

combusted in a low-severity surface fire. The difference,

however, is smaller than that suggested by some authors

(eg Hurteau et al. 2008). Even under the most extreme

fuel-moisture conditions, the water content of live wood

frequently prohibits combustion beyond surface char; this

is evident in the retention of even the smallest canopy

branches after high-severity burns (Campbell et al. 2007).

Moreover, the consumption of fine surface fuels (ie leaf

litter, fallen branches, and understory vegetation),

though variable, can be high even in low-severity burns.

As shown in Figure 1, Campbell et al. (2007) found that

patches of mature mixed-conifer forest in southwestern

Oregon that were subject to low-severity fire (ie 0–10%

overstory mortality) released 70% as much C per unit

area as did locations experiencing high-severity fire (ie >

80% overstory mortality). When scaled over an entire

wildfire perimeter, the importance of high-severity fire in

driving pyrogenic emissions is further diminished because

crown fires are generally patchy while surface fires are

nearly ubiquitous (Meigs et al. 2009). 

According to Campbell et al. (2007), less than 20% of

the estimated 3.8 teragrams of C released to the atmosphere by the 2002 Biscuit Fire in the Siskiyou National

Forest of southern Oregon and northern California

(Figure 1) arose from overstory combustion. Simply put,

because most pyrogenic emissions arise from the combustion of surface fuels, and most of the area within a typical

wildfire experiences surface-fuel combustion, efforts to

minimize overstory fire mortality and subsequent necromass decay are limited in their ability to reduce fire-wide

pyrogenic emissions.

The total amount of biomass combusted, or taken offsite, during a fuel treatment is, by definition, a prescribed

quantity and can vary widely depending on the specific

management objective and techniques used. A review of

fuel-reduction treatments carried out in semiarid conifer

forests in the western US reveals that aboveground C

losses associated with treatment averaged approximately

10%, 30%, and 50% for prescribed fire only, thinning

only, and thinning followed by prescribed fire, respectively (WebTable 1). By comparison, wildfires burning

over comparable fire-suppressed forests consume an average 12–22% of the aboveground C (total fire-wide averages reported by Campbell et al. [2007] and Meigs et al.

[2009], respectively).

Given that both fuel-reduction treatments and wildfire

remove C from a forest, to what degree does the former

reduce the impact of the latter? To test this question,

Mitchell et al. (2009) simulated wildfire combustion following a wide range of fuel-reduction treatments for three

climatically distinct conifer forest types in Oregon. As

illustrated in Figure 2, fuel treatments were effective in

reducing combustion in a subsequent wildfire, and the

greater the treatment intensity, the greater the reduction

in future combustion. However, even in the mature, firesuppressed ponderosa pine (Pinus ponderosa) forest, protecting one unit of C from wildfire combustion typically

came at the cost of removing three units of C in treatment. The reason for this is simple: the efficacy of fuelreduction treatments in reducing future wildfire emissions comes in large part by removing or combusting

surface fuels ahead of time. Furthermore, because removFigure 1. Sources of pyrogenic emissions across the 2002 Biscuit

Fire in southwestern Oregon and northern California. Because

most emissions arise from the combustion of ground and surface

fuels, pyrogenic emissions from high-severity fires were only onethird higher than those in low-severity fires. Moreover, because

most of the fire burned with low severity, the contribution of

high-severity fire to total emissions was only about 20%. The

Biscuit Fire burned over a mosaic of young, mature, and oldgrowth stands of mixed conifer growing across a climate gradient

ranging from mesic to semiarid. Methods are described in

Campbell et al. (2007).

Standing fuels

Ground and surface fuels

Pyrogenic emissions

(kg C m–2)

40

30

20

10

0

3

2

1

0

Pyrogenic emissions

(Tg C)

(thousand ha)

Area burned

150

100

50

0

Low Mod High

Burn severity class
JL Campbell et al. Forest fuels, wildfire, and carbon

© The Ecological Society of America www.frontiersinecology.org

ing fine canopy fuels (ie leaves and twigs) practically necessitates removing the branches and

boles to which they are attached, conventional

fuel-reduction treatments usually remove more C

from a forest stand than would a wildfire burning

in an untreated stand. In an extreme modeling

scenario, wherein only fine-surface fuels were

removed, subsequent avoided combustion did

slightly exceed treatment removals (Figure 2, circles). However, this marginal gain amounted to

less than 0.03% of the total C stores, which is,

practically speaking, a zero-sum game.

n Wildfire probability, treatment life span, and

treatment efficacy across a landscape

Any approach to C accounting that assumes a

wildfire burn probability of 100% during the

effective life span of a fuel-reduction treatment is

almost certain to overestimate the ability of such

treatments to reduce pyrogenic emissions on the

future landscape. Inevitably, some fraction of the

land area from which biomass is thinned will not

be exposed to any fire during the treatment’s

effective life span and therefore will incur no

benefits of reduced combustion (Rhodes and

Baker 2008). On the other hand, assuming that

landscape-wide burn probabilities apply to all of

the treated area is almost certain to underestimate the influence of treatment on future landscape combustion. This is because doing so does

not account for managers’ ability to target treatments

toward probable ignition sources or the capacity of

treated areas to reduce burn probability in adjacent

untreated areas (Ager et al. 2010).

Among fire-prone forests of the western US, the combination of wildfire starts and suppression efforts result in

current burn probabilities of less than 1% (WebTable 2).

Given a fuel-treatment life expectancy of 10–25 years,

only 1–20% of treated areas will ever have the opportunity to affect fire behavior. Such approximations are consistent with a similar analysis reported by Rhodes and

Baker (2008), who suggested that only 3% of the area

treated for fuels is likely to be exposed to fire during their

assumed effective life span of 20 years. Extending treatment efficacy by repeated burning of understory fuels

could considerably increase the likelihood of a treated

stand to affect wildfire behavior, but such efforts come at

the cost of more frequent C loss.

A more robust, though more complicated, evaluation of

fuel-treatment effect on landscape burn probability is

achieved through large-scale, spatially explicit fire spread

simulations (Miller 2003; Syphard et al. 2011). In one

such simulation, representing both the topography and

distribution of fuels across a fire-prone and fire-suppressed

landscape in western Montana, Finney et al. (2007)

showed how strategically treating as little as 1% of the

forest annually for 20 years reduced the area impacted by

a single large wildfire (expected to occur about once on

this landscape in that 20-year period) by half, and how

strategically treating 4% of the forest annually reduced

the area impacted by a single large wildfire by > 95%

(Figure 3). However, even when the treatment effect was

highest, the protection of each hectare of forest from fire

came at the cost of treating nearly 10 hectares (note the

axis scales in Figure 3). Such inefficiencies come not from

the treatments’ efficacy in curtailing fire spread; rather,

they stem from the rarity of wildfire. Put another way, the

treatment of even modest areas may lead to high fractional reductions in the area impacted by high-severity

wildfire, but because such fires rarely affect much of the

landscape, the absolute change in area burned is small. 

n Carbon dynamics through an entire disturbance

cycle

Although there is a body of literature that separately

quantifies the decomposition of standing dead trees, dead

tree fall rate, and the decomposition of downed woody

debris, there are surprisingly few empirical studies that

integrate these processes to estimate the overall longevity

of fire-killed trees. Combining disparate estimates of

standing and downed wood decay with tree-fall rates sugFigure 2. Simulated effectiveness of various fuel-reduction treatments in

reducing future wildfire combustion in a ponderosa pine (Pinus

ponderosa) forest. In general, protecting one unit of C from wildfire

combustion came at the cost of removing approximately three units of C in

treatment. At the very lowest treatment levels, more C was protected from

combustion than removed in treatment; however, the absolute gains were

extremely low. Circles show understory removal, squares show prescribed

fire, and triangles show understory removal and prescribed fire. Simulations

were run for 800 years with a treatment-return interval of 10 years and a

mean fire-return interval of 16 years. Forest structure and growth were

modeled to represent mature, semiarid ponderosa pine forest growing in

Deschutes, Oregon. Further descriptions of these simulations are given in

Mitchell et al. (2009).

0 10 20 30 40

Carbon removed in treatment (Mg C ha–1)

Avoided emissions in subsequent

wildfire (Mg C ha–1)

12

10

8

6

4

2

0

1:1
Forest fuels, wildfire, and carbon JL Campbell et al.

www.frontiersinecology.org © The Ecological Society of America

gests that the overall rate at which fire-killed trees

decompose in a semiarid conifer forest likely ranges

between 1–9% annually (ie a half-life of 8–70 years).

These values are consistent with the observations of

Donato (unpublished data), who found that 52% of the

biomass killed in a forest-replacing wildfire in southwestern Oregon was still present after 18 years.

It is reasonable to expect that in the first decade or two

after a forest-replacing fire, the decomposition of firekilled trees may exceed the net primary production

(NPP) of re-establishing vegetation, thus driving net

ecosystem production (NEP) below zero. This expectation is supported by eddy covariance flux measurements

(Dore et al. 2008) and other empirical studies of post-fire

vegetation (Irvine et al. 2007; Meigs et al. 2009).

However, despite a protracted period of negative NEP following a fire event, total C stocks integrated over the

entire disturbance cycle may be similar for a forest subject

to a fuel-reduction treatment and one subject to a standreplacing fire. This can easily be shown with a simple C

model that simulates growth, mortality, decomposition,

and combustion for ponderosa pine forests (Figure 4).

How can this be? Simply put, biomass recovery may be

slower in the wildfire scenario than in the fuel-reduction

scenario, but initial biomass losses may be greater in the

fuel-reduction scenario than in the wildfire scenario.

Although the parameters used to generate Figure 4 (ie

30% live basal-area removal in the treatment scenario,

100% tree mortality in the wildfire scenario, and rapid

post-fire regeneration) are reasonable, real-world

responses may not exhibit such parity in integrated C

stocks between disturbance types. The point of this simulation is to demonstrate how marked differences in postdisturbance NEP do not necessarily translate into differences in C stocks integrated over time. The

quantification of NEP over short intervals is extremely

valuable in teasing apart ecosystem C dynamics; however,

Figure 3. Simulated effects of strategically placed fuel treatments

on wildfire spread across a fire-prone ponderosa and lodgepole

pine (Pinus contorta) landscape in western Montana. Treating

only 1% of the forest annually for 20 years reduced the area

impacted by a single large wildfire (assumed to occur about once

in 20 years) by more than half. However, across this entire

treatment response, the protection of one hectare of forest from

fire required the treatment of about 10 hectares. Adapted from

Finney et al. (2007). Area burned (ha) during one fire in 20 years

Percentage of a 10 000-ha landscape

treated annually for 20 years

1 2 3 4 5

0 2000 4000 6000 8000 10 000

Area treated after 20 years (ha)

800

600

400

200

0

Figure 4. (a) Simulated net ecosystem production and (b–c) C

stocks throughout an entire disturbance interval, initiated by either

wildfire or fuel-reduction treatment. Unlike the stand subject to

fuel reduction via thinning, the combination of low biomass and

high necromass after wildfire functions to drive NEP below zero.

Nevertheless, although initial losses associated with wildfire were

much lower than those in the fuel-reduction treatment, the two

scenarios achieved parity in C stocks over the entire disturbance

interval. The model used to generate these simulations was

parameterized for a ponderosa pine forest representative of the

eastern Cascades and is fully described in WebFigure 1.

NEP (kg C m–2 yr–1) Stand-level carbon stocks (kg C m–2)

(a) Fuel

reduction

Wildfire

0.1

0.0

–0.1

–0.2

10

5

0

10

5

0

Fuel reduction (b)

Mean over cycle = 6.3 System total

Forest biomass

Forest necromass

Wood products

Wildfire (c)

Mean over cycle = 6.2 System total

Forest biomass

Forest necromass

Wood products

100 200 300

Years
JL Campbell et al. Forest fuels, wildfire, and carbon

© The Ecological Society of America www.frontiersinecology.org

simply comparing C flux rates immediately following different disturbances can give a misleading

picture of how disturbances dictate long-term C

balance.

n Fire frequency and C stocks over multiple

disturbance cycles

The C stocks of an ecosystem in a steady state are

inversely proportional to the rate constants

related to losses, such as those that occur through

respiration or combustion (Olson 1963). Whereas

Olson (1963) considered ecosystems in steady

state, the same phenomenon occurs for the average ecosystem stocks over time or over broad areas

(Smithwick et al. 2007). As fire frequency

increases, the absolute and relative amount of C

combusted per individual fire decreases, suggesting that as fire frequency increases, so too will

average C stocks. However, using a model that

simulates forest growth, mortality, decomposition,

and fuel-dependent combustion, researchers can

show that a low-frequency, high-severity fire

regime stores substantially more C over time than

a high-frequency, low-severity fire regime (mean

C stocks increased by 40% as the mean fire-return

interval was increased from 10 to 250 years; Figure

5). The reason for this is explained by the first

principles outlined by Olson (1963). Fractional

combustion is, by nature, more constrained than

fire frequency. In our example, although fire interval increased from 10 years to 250 years, fractional

combustion of ecosystem C for a semiarid ponderosa pine forest only increased from 9% to 18%

(Figure 5). To have parity in C stocks across these

different fire intervals, fractional combustion per

event would, at times, have to exceed 250% –

clearly violating the conservation of mass. As long

as wildfire does not cause lasting changes in site

productivity or non-fire mortality, no forest system

is exempt from this negative relationship between

fire frequency and average landscape C storage. Although

we chose to illustrate the response for a semiarid ponderosa pine forest typical of those considered for fuel

reduction, the same relative response was observed when

the simulations were run for mesic Douglas fir

(Pseudotsuga menziesii) forests parameterized for higher

production and decomposition rates.

Although stability of C stocks is desirable, stability is a

function of spatial extent. In the case of a single forest

stand, C stocks under the frequent, low-severity fire

regime are more stable than those under an infrequent,

high-severity fire regime. However, the fluctuations in C

stocks exhibited by a single stand become less relevant as

one scales over time or over populations of stands experiencing asynchronous fire events (Smithwick et al. 2007).

In other words, forests experiencing frequent fires lose

less C per fire event than forests experiencing infrequent

fires, but the former do not store more C over time or

across landscapes. 

n The capacity of fire and fuel-reduction treatments

to alter equilibrium states

In the sections above, we have assumed that forests eventually succeed toward a site-specific dynamic equilibrium

of growth and mortality. Although the concept of a sitespecific carrying capacity usefully underlies many of the

models of forest development, it is worth considering situations where disturbances might initiate alternate

steady states by effecting changes in growth, mortality, or

combustibility that persist through to the next disturbance.

Figure 5. Total forest C stores simulated for a ponderosa pine forest in the

eastern Cascades of Oregon experiencing three different hypothetical fire

regimes. Black lines depict the C stores of five individual stands subject to

random fire events. Blue lines mark the 500-year average of all five

stands. As mean fire-return interval increases, the variation of C stores

over time (or space by extension) increases, but so does the long-term

average. For simplicity, we show the results of only five stands per fire

regime; however, the mean trends do not change with additional

simulations. Nearly identical patterns result when alternate forest types

are used. We performed simulations using STANDCARB, as described

in WebFigure 2 and in Harmon et al. (2009). Total forest carbon (Mg C ha–1)

400

300

200

100

0

400

300

200

100

0

400

300

200

100

0

223

Mean fire-return interval = 10 years

291

Mean fire-return interval = 50 years

327

Mean fire-return interval = 250 years

0 100 200 300 400 500

Simulation time (years)
Forest fuels, wildfire, and carbon JL Campbell et al.

www.frontiersinecology.org © The Ecological Society of America

A simple example of disturbance-altering, long-term

forest growth involves the loss in soil fertility that can

accompany certain high-severity fires (Johnson and

Curtis 2001; Bormann et al. 2008). Another mechanism

by which disturbance can initiate changes in steady-state

C stocks involves the persistent changes in tree density

that may follow some disturbance events. For instance,

Kashian et al. (2006) determined that forest biomass in

the lodgepole pine (Pinus contorta) forests of Yellowstone

National Park was relatively insensitive to changes in fire

frequency but very dependent on the density to which

forests grew after fire. In a system where long-term successional trajectories are contingent more on forest condition at the time of disturbance (eg serotinous seed availability) than on permanent site conditions, C stocks

could well stabilize at different levels after different disturbances, as illustrated in Figure 6a.

A final example of how changes in disturbance regime

could persistently alter equilibrium between growth and

mortality involves size-dependent mortality in the semiarid conifer forests of the Sierra Nevada (Smith et al.

2005). Both Fellows and Goulden (2008) and North et al.

(2009) found fewer large trees and lower

overall biomass in current fire-excluded

forests than were believed to exist at these

locations before fire exclusion. These

authors suggest that small trees are disproportionally vulnerable to fire mortality, and

large trees are disproportionally vulnerable

to pathogen- and insect-based mortality;

therefore, as biological agents replace fire

as the primary cause of mortality, the number of large trees decreases accordingly.

Under such scenarios, the thinning of small

trees combined with frequent burning

could, over time, increase biomass by maintaining a greater number of larger trees (see

Figure 6b). However, not all studies support

the notion that fire exclusion reduces

stand-level biomass (Bouldin 2008;

Hurteau et al. 2010). Specifically, another

study conducted by North et al. (2007) in

the Sierra Nevada found that net losses in

large-diameter trees between 1865 and

2007 were more than compensated for by

the infilling of small-diameter trees, such

that total live-wood volume remained

unchanged over this period of fire suppression. Furthermore, Hurteau and North

(2010) reported that fire-suppressed control plots aggraded as much C over 7 years

as did comparable thinned plots.

Presuming that maximum steady-state C

stocks are not dictated entirely by permanent site qualities and depend, at least in

some part, on the nature and timing of disturbance, it is conceivable that prescriptions such as fuel reduction and prescribed fire could

eventually elevate (or reduce) C stocks at a single location slightly beyond what they would be under a different

disturbance regime (Hurteau et al. 2010). However,

exactly how stable or self-reinforcing this alternate state

is remains unknown.

n Additional considerations

The purpose of this paper is to illustrate the basic biophysical relationships that exist between fuel-reduction

treatments, wildfire, and forest C stocks over time.

Understanding these dynamics is necessary for crafting

meaningful forest C policy; however, it is not by itself sufficient. A full accounting of C would also include the fossil-fuel costs of conducting fuel treatments, the longevity

of forest products removed in fuel treatments, and the

ability of fuel treatments to produce renewable “bioenergy”, potentially offsetting combustion of fossil fuels. A

detailed consideration of these factors is beyond the scope

of this paper, but it is worth pointing out some limits of

their contribution. First, the fossil-fuel costs of conductFigure 6. Hypothetical examples of how disturbances could initiate alternate

steady-state C stocks. (a) Illustration of what C stocks might look like if long-term

successional trajectories were contingent more on seed availability at the time of

fire than they were on fixed site conditions, as suggested by Kashian et al.

(2006). (b) Illustration of how frequent fires could shift mortality away from

larger trees and toward smaller trees, thus increasing steady-state C stocks, as

suggested by North et al. (2009).

(a)

Carbon stocks

Mass per affected area

(b)

Disturbance Disturbance Disturbance

Time

Frequent fire where fire-driven mortality

disproportionally affects small trees

Prolonged fire exclusion

where drought-driven mortality

disproportionally affects large trees
JL Campbell et al. Forest fuels, wildfire, and carbon

ing fuel treatments are relatively small, ranging from

1–3% of the aboveground C stock (Finkral and Evans

2008; North et al. 2009; Stephens et al. 2009b). Second,

only a small fraction of forest products ever enters “permanent” product stocks; this is especially true for the

smaller-diameter trees typically removed during fuel

treatments. Primarily, half-lives of forest products (7–70

years) are not significantly different than the half-life of

the same biomass left in forests (Krankina and Harmon

2006). Third, the capacity of forest biofuels to offset C

emissions from fossil-fuel consumption is greatly constrained by both transportation logistics and the lower

energy output per unit C emitted as compared with fossil

fuel (Marland and Schlamadinger 1997; Law and

Harmon 2011).

n Conclusions

The empirical data used in this paper derive from semiarid, fire-prone conifer forests of the western US, which

are largely composed of pine, true fir (Abies spp), and

Douglas fir. These are the forests where management

agencies are weighing the costs and benefits of up-scaling

fuel-reduction treatments. Although it would be imprudent to insist that the quantitative responses reported in

this paper necessarily apply to every manageable unit of

fire-prone forest in the western US, our conclusions

depend not so much on site-specific parameters but rather

on the basic relationships – between growth, decomposition, harvest, and combustion – to which no forest is

exempt. To simply acknowledge the following – that (1)

forest wildfires primarily consume leaves and small

branches, (2) even strategic fuels management often

involves treating more area than wildfire would otherwise

affect, and (3) the intrinsic trade-off between fire frequency and the amount of biomass available for combustion functions largely as a zero-sum game – leaves little

room for any fuel-reduction treatment to result in greater

sustained biomass regardless of system parameterization.

Only when treatment, wildfire, or their interaction leads

to changes in maximum biomass potential (ie system

state change) can fuel treatment profoundly influence C

storage. 

In evaluating the effects of wildfire and fuel-reduction

treatments on forest C stocks across various spatial and

temporal scales, we conclude that:

(1) Empirical evidence shows that most pyrogenic C

emissions arise from the combustion of surface fuels,

and because surface fuel is combusted in almost all

fire types, high-severity wildfires burn only 10% more

of the standing biomass than do the low-severity fires

that fuel treatment is intended to promote (Figure 1). 

(2) Model simulations support the notion that forests

subjected to fuel-reduction treatments experience less

pyrogenic emissions when subsequently exposed to

wildfires. However, across a range of treatment inten-

© The Ecological Society of America www.frontiersinecology.org

sities, the amount of C removed in treatment was typically three times that saved by altering fire behavior

(Figure 2).

(3) Fire-spread simulations suggest that strategic application of fuel-reduction treatments on as little as 1% of

a landscape annually can reduce the area subject to

severe wildfire by 50% over a 20-year period. Even so,

the protection of one hectare of forest from wildfire

required the treatment of 10 hectares, owing not to

the low efficacy of treatment but rather to the rarity

of severe wildfire events (Figure 3). 

(4) It is reasonable to expect that after a forest-replacing

fire, the decomposition of fire-killed trees exceeds

NPP, driving NEP below zero. By contrast, the deliberate removal of necromass in fuel-reduction treatments could result in a period of elevated NEP.

However, despite marked differences in post-disturbance NEP, it is possible for average C stocks to be

identical for these two disturbance types (Figure 4).

(5) Long-term simulations of forest growth, decomposition, and combustion illustrate how, despite a negative feedback between fire frequency and fuel-driven

severity, a regime of low-frequency, high-severity fire

stores more C over time than a regime of high-frequency, low-severity fire (Figure 5).

(6) The degree to which fuel management could possibly

lead to increased C storage over space and time is

contingent on the capacity of such treatments to

increase maximum achievable biomass through

mechanisms such as decreased non-fire mortality or

the protection from losses in soil fertility that are

sometimes associated with the highest-severity fires

(Figure 6).

There is a strong consensus that large portions of forests

in the western US have suffered both structurally and

compositionally from a century of fire exclusion and that

certain fuel-reduction treatments, including the thinning

of live trees and prescribed burning, can be effective tools

for restoring historical functionality and fire resilience to

these ecosystems (Hurteau et al. 2010; Meigs and Campbell 2010). Furthermore, by reducing the likelihood of

high-severity wildfire, fuel-reduction treatments can

improve public safety and reduce threats to the resources

provided by mature forests.

On the basis of material reviewed in this paper, it

appears unlikely that forest fuel-reduction treatments

have the additional benefit of increasing terrestrial C

storage simply by reducing future combustive losses and

that, more often, treatment would result in a reduction in

C stocks over space and time. Claims that fuel-reduction

treatments reduce overall forest C emissions are generally

not supported by first principles, modeling simulations, or

empirical observations. The C gains that could be

achieved by increasing the proportion of large to small

trees in some forests are limited to the marginal and variable differences in biomass observed between fire-sup-
Forest fuels, wildfire, and carbon JL Campbell et al.

pressed forests and those experiencing frequent burning

of understory vegetation. 

Emerging policies aimed at reducing atmospheric CO2

emissions may well threaten land managers’ ability to

apply restoration prescriptions at the scale necessary to

achieve and sustain desired forest conditions. For this reason, it is imperative that scientists continue research into

the processes by which fire can mediate long-term C storage (eg charcoal formation, decomposition, and community state change) and more accurately quantify the unintended consequences of fuel-reduction treatments on

global C cycling.

n Acknowledgements

This work was funded, in part, by the US Forest Service,

Western Wildland Environmental Threat Assessment

Center.

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fluxes in semiarid ponderosa pine in Central Oregon. Glob

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www.frontiersinecology.org © The Ecological Society of America
© The Ecological Society of America www.frontiersinecology.org

JL Campbell et al. – Supplemental information 

WebTable 1. Biomass reductions associated with various fuel reduction treatments as prescribed at various

fire-prone forests of western North America 

Δ total aboveground

Fraction of live Δ surface biomass through both

basal area cut or fuels combustion and

killed in Fate of (estimated removal (estimated

Treatment type, forest type, and location prescribed burn logging slash fraction) fraction)

Prescribed fire only

Sierran mixed conifer (Central Sierras)a 0.00 None –0.70 –0.11

Sierran mixed conifer (Central Sierras)b 0.15 None –0.02 –0.13

Ponderosa pine/Douglas fir (Northern Rockies)b 0.11 None –0.19 –0.12

Ponderosa pine/Douglas fir (Blue Mountains)b 0.08 None –0.32 –0.12

Ponderosa pine (Southwestern Plateau)b 0.04 None –0.50 –0.11

Ponderosa pine/true fir (Southern Cascades)b 0.30 None 0.67 –0.16

Thinning only

Sierran mixed conifer (Central Sierras)a 0.36 Left on site 0.96 –0.16

Sierran mixed conifer (Central Sierras)a 0.60 Left on site 1.60 –0.27

Ponderosa pine (Southern Rockies)c 0.36 Pile burned 0.01 –0.30

Ponderosa pine (Central Sierras)d 0.50 Pile burned 0.01 –0.42

Sierran mixed conifer (Central Sierras)b 0.34 Left on site 0.92 –0.15

Ponderosa pine/Douglas fir (Northern Rockies)b 0.54 Left on site 1.43 –0.24

Ponderosa pine/Douglas fir (Blue Mountains)b 0.24 Left on site 0.64 –0.11

Ponderosa pine (Southwestern Plateau)b 0.53 Pile burned 0.01 –0.45

Ponderosa pine/true fir (Southern Cascades)b 0.58 Removed 0.00 –0.49

Thinning and prescribed fire

Sierran mixed conifer (Central Sierras)a 0.37 Left on site –0.40 –0.38

Sierran mixed conifer (Central Sierras)a 0.66 Left on site –0.17 –0.59

Ponderosa pine (Central Sierras)d 0.50 Pile burned –0.69 –0.53

Sierran mixed conifer (Central Sierras)b 0.42 Left on site –0.37 –0.41

Ponderosa pine/Douglas fir (Northern Rockies)b 0.78 Left on site –0.08 –0.67

Ponderosa pine/Douglas fir (Blue Mountains)b 0.46 Left on site –0.33 –0.44

Ponderosa pine (Southwestern Plateau)b 0.59 Pile burned –0.68 –0.61

Ponderosa pine/true fir (Southern Cascades)b 0.73 Removed –0.70 –0.72

Notes: Total biomass losses were approximated solely from basal reported area reduction according to the following assumptions: total aboveground biomass was

assumed to be composed of 45% live merchantable boles (subject to removal proportional to basal area reduction), 40% live tree branch and foliage (converted to slash

proportional to basal area reduction), and 15% surface fuels (both live and dead biomass and subject to combustion in prescribed fire). Prescribed fire was assumed to

combust 70% of surface fuels and logging slash; pile burning was assumed to combust 99% of logging slash. a

North et al. (2007); bStephens et al. (2009); cFinkal and Evans

(2008); dCampbell et al. (2008).
Supplemental information JL Campbell et al.

www.frontiersinecology.org © The Ecological Society of America

WebTable 2. Burn probability for forests of Oregon, Washington, and California from 1985 to 2005 

Fraction of forest area Random probability of a

burned annually Fuel-treatment treated stand

Any High life expectancy being exposed

Forest type (ecoregion) severity severity (range in years) to any fire

Cool–wet conifer 0.00018 0.00002 5–15 0.0009–0.00274

(Coast Range)

Cool–mesic conifer 0.00177 0.00046 5–15 0.00884–0.02651

(West Cascades, North Cascades)

Cool–dry conifer 0.00411 0.00054 10–25 0.04112–0.10279

(East Cascades, North Rockies, Blue Mts)

Warm–mesic conifer 0.00622 0.00119 10–25 0.06217–0.15542

(Klamath Mountains)

Warm–dry conifer 0.00780 0.00178 10–25 0.07798–0.19495

(Sierra Nevada, South California Mts)

Notes: This simple prediction of wildfire-treatment occurrence by multiplying regional fire probability by fuel treatment life assumes random interaction of wildfire and

treatment and does not account for strategic placement of fuel treatments. Area burned annually based on Monitoring Trends in Burn Severity fire perimeter and severity classification maps from 1985 to 2005 (http://mtbs.gov). Total forested area in each ecoregion based on 2005 National Land Cover Dataset land-cover maps

(http://landcover.usgs.gov). Ecoregions correspond to Omernik Level 3 classification (Omernik 1978). Treatment life expectancies are crude estimates based on Rhodes

and Baker (2008) and Agee and Skinner (2005). Being that these numbers were derived from actual region-wide land-surface-change detection, they include regional fire

suppression activities. Natural burn probabilities, as well as those that may result from future management decisions or climate change, are likely to be higher.

WebReferences 

Agee JK and Skinner CN. 2005. Basic principles of forest fuel

reduction treatments. Forest Ecol Manag 211: 83–96.

Campbell JC, Alberti G, Martin JG, and Law BE. 2008. Carbon

dynamics of a ponderosa pine plantation following fuel

reduction treatment in the northern Sierra Nevada. Forest

Ecol Manag 275: 453–63.

Finkral AJ and Evans AM. 2008. The effects of a thinning

treatment on carbon stocks in a northern Arizona ponderosa pine forest. Forest Ecol Manag 255: 2743–50.

Harmon ME, Moreno A, and Domingo BJ. 2009. Effects of partial harvest and site preparation on the carbon stores in

Douglas-fir/western hemlock forests: a simulation study.

Ecosystems 12: 777–91.

North M, Innes J, and Zald H. 2007. Comparison of thinning

and prescribed fire restoration treatments to Sierran mixedconifer historic conditions. Can J Forest Res 37: 331–42.

Omernik JM. 1987. Map supplement: ecoregions of the conterminous United States. Ann Assoc Am Geogr 77: 118–25.

Rhodes JJ and Baker WL. 2008. Fire probability, fuel treatment

effectiveness and ecological tradeoffs in western US public

forests. The Open Forest Science Journal 1: 1–7.

Stephens SL, Moghaddas JJ, Edminster C, et al. 2009. Fire

treatment effects on vegetation structure, fuels, and potential fire severity in western US forests. Ecol Appl 19:

305–20.
JL Campbell et al. Supplemental information

© The Ecological Society of America www.frontiersinecology.org

WebFigure 1. (a) Structure and (c and d) dynamic functions behind the forest carbon model

used to produce Figure 4. (b) Live biomass is assumed to aggrade over time according to a

Chapman-Richards function y1 = a*(1 – exp[–b1x1])

c

, the derivative of which, y2 =

c*b1*y1*(1 – exp[ln{y1/a}/c])/exp(ln[y1/a]/c), allows (c) ANPP (aboveground net primary

production) to be calculated annually according to current biomass; y1 is aboveground live

biomass in kg C m–2, y2 is ANPP in kg C m–2 yr–1, a is the maximum aboveground live biomass

that the site can sustain, x1 is the time in years since initiation (which can be back-calculated

from any assigned biomass), b1 is a constant proportional to the time required to achieve

maximum biomass, and c is a constant proportional to the initial growth lag. (d)

Decomposition, the heterotrophic mineralization of each necromass pool including wood

products, is determined according to an exponential loss function y4 = M* – k, where y4 is loss

of necromass in kg C m–2 yr–1, M is the current mass of necromass in kg C m–2, and k is a poolspecific decomposition constant. For the simulations shown in Figure 4, we used the following

parameters to represent growth, harvest, combustion, and decay in a semiarid, fire-prone pine

forest of western North America: a = 4.8 kg C m–2; b1 = 0.02; c = 1.6; k = 0.005 yr–1 for

both forest necromass and wood products; starting y1 = 4.5 kg C m–2; starting Mnecromass = 2.2

kg C m–2; starting Mproducts = 0 Mg C ha–1; treatment removals = 2.9 kg C m–2; treatment

related mortality (uncombusted slash) = 1% of y1 at time of treatment; wildfire mortality =

95% of y1 at time of fire; wildfire combustion = 10% of y1 and 40% M of at time of fire.

(a) (b)

(c) (d)

CO2

NPP

Live

biomass

harvest mortality

Wood

products

Forest

necromass

combustion decomposition decomposition

combustion combustion

CO2

Max

Live biomass

0.0

0 100 200

Years since initiation

1.0

0.5

0.0

0 0.5 1.0

Aboveground biomass

(fraction of max)

0 100 200

Years since mortality

1.0

0.5

0.0

Fraction of dead C remaining

ANPP (fraction of max)
Supplemental information JL Campbell et al.

www.frontiersinecology.org © The Ecological Society of America

WebFigure 2. (a) Structure and (b and c) disturbance responses behind STANDCARB, the

forest carbon model used to produce Figure 5. STANDCARB simulates the accumulation of C

over succession in mixed-species and mixed-age forest stands at annual time steps. The growth

of vegetation and subsequent transfer of C among the various carbon pools shown in (a) are

regulated by user-defined edapho-climatic inputs and species-specific responses. The imposition

of wildfire in any given year results in the instantaneous transfer of C from each live pool into

its corresponding dead pool (wildfire mortality) and the instantaneous loss of C from each live

and dead pool to the atmosphere (wildfire combustion). The exact amount of mortality and

combustion incurred in a given wildfire depends on stand-specific species composition, and the

amount of biomass in each separate C pool, which at any given time may not be in equilibrium

(gray circles in [b] and [c] reflect this variation). For the simulations shown in Figure 5,

STANDCARB was parameterized for a semiarid ponderosa pine forest growing in eastern

Oregon: max attainable biomass = 210 Mg C ha–1; mean ANPP = 5.1 Mg C ha–1 yr–1; nonfire mortality rate constants = 0.37, 0.5, 0.032, 0.017, and 0.013 yr–1 for foliage, fine roots,

branches, coarse roots, and stems, respectively; decomposition rate constants = 0.21, 0.15,

0.08, 0.11, 0.023, and 0.017 yr–1 for foliage, fine roots, branches, coarse roots, stems, and

soil C, respectively. It is worth noting that patterns nearly identical to those illustrated in Figure

5 result from STANDCARB parameterized for a mesic Douglas-fir forest having much larger

ANPP, potential biomass, and decomposition rates. For a full description of STANDCARB

structure and parameterization, see Harmon et al. (2009) and http://andrewsforest.oregon

state.edu/lter/pubs/webdocs/models/standcarb2.htm.

(c)

(a)

(b)

Upper tree

Lower tree

Shrub

Herb

foliage

branch

sapwood

heartwood

heartrot

C-roots

F-roots

dead foliage

dead branch

dead sapw

dead heartw

dead C-root

dead F-root

stable foliage

stable wood

stable soil

charcoal

Wildfire combustion

(fraction of total C stores)

Wildfire mortality

(fraction of live mass)

A stand made of grid cells 0.3

0.2

0.1

0.0

1.0

0.5

0.0

0 100 200 300 400

Years since last wildfire