CSIRO PUBLISHING
International Journal of Wildland Fire 2012, 21, 306–312
http://dx.doi.org/10.1071/WF11019
Fuel loadings 5 years after a bark beetle outbreak
in south-western USA ponderosa pine forests
Chad M. HoffmanA,E, Carolyn Hull Sieg B, Joel D. McMillinC and Peter Z. Fulé D
A
Wildland Fire Program, College of Natural Resources, University of Idaho, Moscow,
ID 83844, USA.
B
USDA Forest Service, Rocky Mountain Research Station, 2500 Pine Knoll Drive, Flagstaff,
AZ 86001, USA. Email: csieg@fs.fed.us
C
USDA Forest Service, Region 3 Forest Health Protection, 2500 Pine Knoll Drive, Flagstaff,
AZ 86001, USA. Email: jmcmillin@fs.fed.us
D
School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA.
Email: pete.fule@nau.edu
E
Corresponding author. Present address: Department of Forest and Rangeland Stewardship,
Warner College of Natural Resources, Colorado State University, Fort Collins, CO 80523, USA.
Email: c.hoffman@colostate.edu
Abstract. Landscape-level bark beetle (Coleoptera: Curculionidae, Scolytinae) outbreaks occurred in Arizona ponderosa pine (Pinus ponderosa Dougl. ex Law.) forests from 2001 to 2003 in response to severe drought and suitable forest
conditions. We quantified surface fuel loadings and depths, and calculated canopy fuels based on forest structure attributes
in 60 plots established 5 years previously on five national forests. Half of the plots we sampled in 2007 had bark beetlecaused pine mortality and half did not have mortality. Adjusting for differences in pre-outbreak stand density, plots with
mortality had higher surface fuel and lower canopy fuel loadings 5 years after the outbreak compared with plots without
mortality. Total surface fuels averaged 2.5 times higher and calculated canopy fuels 2 times lower in plots with mortality.
Nearly half of the trees killed in the bark beetle outbreak had fallen within 5 years, resulting in loadings of 1000-h woody
fuels above recommended ranges for dry coniferous forests in 20% of the mortality plots. We expect 1000-h fuel loadings
in other mortality plots to exceed recommended ranges as remaining snags fall to the ground. This study adds to previous
work that documents the highly variable and complex effects of bark beetle outbreaks on fuel complexes.
Additional keywords: Dendroctonus, forest fuels, Ips, Pinus ponderosa, resistance to fire control.
Received 2 February 2011, accepted 20 June 2011, published online 20 December 2011
Introduction
Bark beetles (Coleoptera: Curculionidae, Scolytinae) are
important biotic agents of conifer mortality in forests of western
North America (Furniss and Carolin 1977) and play a key role in
the disturbance ecology of these ecosystems (Fettig et al. 2007).
Increased populations of bark beetles have been observed in
conifer forest types across the western United States in recent
years (Bentz 2009). A recent bark beetle outbreak in the southwestern United States partially caused by drought resulted in
extensive ponderosa pine (Pinus ponderosa Dougl. ex Law.)
mortality throughout Arizona between 2001 and 2003 (USDA
Forest Service 2004; Negrón et al. 2009). Early in the outbreak,
Ips species (I. lecontei Swaine and I. pini Say) were the primary
tree-killing species, in particular at low to mid-elevations,
whereas Dendroctonus species (D. brevicomis LeConte,
D. adjunctus Blandford and D. frontalis Zimmermann) became
increasingly important later in the outbreak at mid- to high
Journal compilation Ó IAWF 2012
elevations (USDA Forest Service 2004; Williams et al. 2008).
Ips species typically attack smaller-diameter ponderosa pine,
compared with Dendroctonus species (Breece et al. 2008).
Potential increases in fire hazard caused by altered fuels complexes following this outbreak have concerned land managers
and the public. Although bark beetle outbreaks likely affect the
subsequent fuels complex (Sánchez-Martı́nez and Wagner
2002; Page and Jenkins 2007a, 2007b; Jenkins et al. 2008), there
is a paucity of scientifically and statistically sound studies on
this topic (Negrón et al. 2008), particularly in ponderosa pinedominated systems.
Bark beetles affect the spatial distribution and condition of
fuels complexes through time by causing live fuel to change to
dead fuel. The change in state from live to dead fuel lowers
canopy fuel moisture and alters the vertical and horizontal
distribution of fuels as tree foliage falls to the forest floor. Thus,
the immediate effect of bark beetles on the fuels complex is not a
www.publish.csiro.au/journals/ijwf
Influence of a bark beetle outbreak on fuel loads
change in the total fuel loadings but rather an alteration in their
spatial distribution and moisture content. However, alterations
to the fuels complex are transitory in nature owing to changes in
vertical and horizontal distribution of fuels as needles and snags
fall and new plants grow in openings. Because rates of needledrop, tree-fall and surface-fuel decomposition vary widely
across elevation gradients and forest types (Cahill 1977; Jenkins
et al. 2008), it is critical to quantify relationships between bark
beetle outbreaks and alterations to the fuels complex across this
variability. Studies examining the relationship between bark
beetles and fuels complexes have been conducted primarily in
lodgepole pine (Pinus contorta Dougl. Ex Loud.) (Brown 1975;
Gara et al. 1985; Amman 1991; Lynch et al. 2006; Page and
Jenkins 2007a, 2007b; Simard et al. 2011), and in mixed-conifer
ecosystems (Cahill 1977; Baker and Veblen 1990; Jenkins et al.
1998; Jenkins et al. 2008). Relationships between bark beetle
outbreaks and fuel loading have not been reported in ponderosa
pine forests (Jenkins et al. 2008).
The goal of this study was to quantify stand structure, surface
fuels and canopy fuels in south-western US ponderosa pine
stands with and without bark beetle-caused tree mortality 5 years
after an outbreak. We hypothesised that plots with mortality
would have lower tree density and canopy fuels but greater
surface fuel loading due to the accumulation of dead woody
material compared with plots without mortality.
Methods
Study sites
We subsampled from a network of 1181 permanent 0.02-ha
plots that Negrón et al. (2009) established in 2003–04 across
five National Forests in Arizona to quantify overstorey effects of
bark beetle outbreaks in ponderosa pine stands. These plots were
located on the Apache–Sitgreaves, Coconino, Kaibab, Prescott
and Tonto National Forests, and were constrained by the distribution of ponderosa pine. The number of sample plots was
proportional to the area of ponderosa pine forest per national
forest and was independent of previous land management history. Negrón et al. (2009) describe the methods used to establish
the original 1181 plots.
From the original 1181 plots, we selected 60 plots, half of
which had at least 20% of the ponderosa pine basal area killed
from 2000 to 2003 and the other half had no mortality. Pairing of
plots was based on geographic proximity (same National Forest), similarity in elevation (75 m), and pre-outbreak overstorey stand composition (10% ponderosa pine). Elevation of
the plots ranged from 1547 to 2240 m, which covers most of the
range of ponderosa pine in Arizona. Other tree species within
the plots included pinyon pine (Pinus edulis Engelm.), Gambel
oak (Quercus gambelii (Nutt.)), Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco), Rocky Mountain juniper
(Juniperus scopulorum (Sarg.)), Utah juniper (J. osteosperma
(Torr.)), oneseed juniper (J. monosperma (Engelm.) Sarg.) and
alligator juniper (J. deppeana (Steud.)).
Following bark beetle tree mortality, we sampled overstorey
attributes in the fixed-radius 0.02-ha circular plots. We sampled
diameter at breast height (DBH, 1.37 m above the ground) of all
trees $10-cm DBH, the lowest live branch height (m) and tree
height (m). In addition, trees were classified as alive or dead, and
Int. J. Wildland Fire
307
assigned a canopy position (dominant, co-dominant, intermediate or suppressed). Pre-outbreak overstorey measurements were
obtained from data originally collected in 2003 and 2004 by
Negrón et al. (2009).
Surface and canopy fuel measurements and calculations
Downed woody material and litter and duff depths were
measured in each plot along two 15.2-m planar transects that
originated from the plot centre running in opposite directions
from each other using Brown’s (1974) methods. Downed
woody material was inventoried by time-lag size classes: 1-h
(0.0–0.64-cm diameter), 10-h (0.65–2.54-cm diameter), 100-h
(2.55–7.62-cm diameter) and 1000-h (.7.62-cm diameter). The
1- and 10-h fuel classes were tallied along the first 1.8 m of
each transect, 100-h fuels were tallied along the first 3.1 m, and
1000-h fuels were tallied along the entire transect. In addition,
diameters of 1000-h fuels were measured and assigned as either
sound or rotten. Duff, litter and fuel-bed (high particle) depth
were measured at 0.3, 6, 9, 12 and 15.2 m on each transect. Fuelbed depth was estimated as the height from the bottom of the
litter layer to the top of the highest fuel particle with a diameter
,7.62 cm that intercepted a 0.3 m-long plane perpendicular
to the main transect. Fuel loadings were then calculated by size
classes on each plot using the algorithms detailed in Brown
(1974).
We inferred live canopy woody fuel loadings by time-lag
size classes (1-, 10-, 100-h) and foliage biomass using allometric
equations developed by Brown (1978). Canopy biomass estimates from Brown (1978) were adjusted using a local multiplication factor for ponderosa pine near Flagstaff, Arizona: 0.45 for
dominant trees, 0.2 for co-dominant trees, 0.15 for intermediate
trees and 0.1 for suppressed trees, developed by Reinhardt et al.
(2006). Available canopy fuels were considered those that could
be consumed during the passing of a crown fire, which we
calculated as the entire foliage biomass plus 50% of the 1-h
canopy biomass (Scott and Reinhardt 2001). Canopy bulk
density was estimated by developing a vertical canopy fuel
profile for each stand (Scott and Reinhardt 2001) and calculated
as the maximum 91-cm running mean within the canopy
(Reinhardt et al. 2006). We estimated canopy base height as
the lowest height at which canopy bulk density was at least
0.012 kg m3 (Reinhardt and Crookston 2003).
Statistical analyses
Pre-outbreak differences between mortality and no-mortality
plots were compared using either a t-test if both normality and
equal variance assumptions were met, an unequal variance
Student’s t-test if normality was met but variances were
heterogeneous, or a Mann–Whitney rank sum test if neither
normality nor equal variances assumptions were met. We used
Shapiro–Wilk’s and Levene’s tests to check for normality and
equal variances respectively. All pre-outbreak variables met
the equal variance assumption but did not have normal distributions, and therefore were analysed using Mann–Whitney
rank sum tests.
Because pre-outbreak tree densities differed significantly
between mortality and no-mortality plots, we used analysis of
covariance (ANCOVA) with pre-outbreak tree density as a
308
Int. J. Wildland Fire
C. M. Hoffman et al.
Table 1. Pre-bark beetle outbreak stand structural characteristics in ponderosa pine stands
Mean (s.e.). Data were analysed using Mann–Whitney rank sum tests. Asterisks (*) indicate significant differences
(P , 0.05)
No-mortality (n ¼ 30)
Mortality (n ¼ 30)
P
29.9 (1.8)
10.5 (0.8)
387.1 (42.2)
279.9 (33.6)
107.2 (23.5)
27.4 (2.80)
17.2 (2.7)
10.3 (2.2)
187.1 (17.2)
24.9 (5.9)
9.0 (0.7)
545.9 (62.5)
399.4 (48.4)
146.5 (22.9)
26.8 (3.2)
20.6 (3.3)
6.2 (1.3)
195.8 (20.5)
0.005*
0.128
0.017*
0.048*
0.101
0.867
0.460
0.417
0.819
Quadratic mean diameter (cm)
Mean tree height (m)
Tree density all species (stems ha1)
Density of ponderosa pine (stems ha1)
Density of other species (stems ha1)
Total basal area (m2 ha1)
Basal area of ponderosa pine (m2 ha1)
Basal area of other species (m2 ha1)
Stand density index
Table 2. Five-years post-outbreak surface fuel loadings and depth in ponderosa pine stands
Adjusted means (s.e.) based on analysis of covariance using pre-outbreak tree density as a covariate. Percentage
difference is calculated difference between no-mortality and mortality plot averages. Asterisks (*) indicate significant
differences (P # 0.05)
2
1-h (kg m )
10-h (kg m2)
100-h (kg m2)
1000-h sound (kg m2)
1000-h rotten (kg m2)
Total 1000-h (kg m2)
Total woody fuel (kg m2)
Fuel-bed depth (cm)
Litter depth (cm)
Duff depth (cm)
No-mortality (n ¼ 30)
Mortality (n ¼ 30)
% difference
P
0.11 (0.01)
0.43 (0.09)
0.33 (0.13)
0.05 (0.38)
0.69 (0.43)
0.72 (0.22)
1.61 (0.36)
6.34 (3.4)
6.8 (2.8)
1.9 (0.2)
0.27 (0.01)
0.69 (0.01)
0.81 (0.01)
1.18 (0.04)
1.52 (0.04)
2.71 (0.06)
4.64 (0.69)
12.20 (4.1)
14.9 (2.7)
1.8 (0.2)
þ140%
þ63%
þ140%
þ2550%
þ119%
þ278%
þ188%
þ92%
þ119%
5%
0.041*
0.022*
0.015*
0.021*
0.065
0.014*
0.006*
0.034*
0.042*
0.947
covariate to analyse post-outbreak variables. Otherwise, with
the exception of quadratic mean diameter, mortality plots did
not differ from no-mortality before the outbreak (Table 1). We
used Shapiro–Wilk’s and Levene’s tests to check for normality
and equal variances respectively. Dependent variables not
meeting assumptions of normality and equal variances were
first transformed using a log-transformation. In situations where
a log-transformation did not meet assumptions, a rank transformation was performed instead. Log-transformed variables
include fuel, litter and duff depths, canopy bulk density, tree
diameters and heights, and all tree density variables; other
variables were not transformed (stand density index), or
required rank transformations. Rank transformations are robust
to violations of normality for ANCOVA and perform well for
moderate sample sizes over a variety of distributions (Olejnik
and Algina 1984). In cases where the covariate was not significant, it was removed from the model.
Results
Surface and canopy fuel loadings
Mortality plots had higher surface fuel loading in all woody fuel
size classes (except total 1000-h rotten fuels), total surface
fuel loading (P , 0.001) and higher fuel-bed depth and litter
depth compared with no-mortality plots (Table 2). A breakdown
of woody surface fuel loadings by size classes indicated that
mortality plots had higher loadings in the 1-, 10-, 100- and 1000-h
fuel classes compared with no-mortality plots (P ¼ 0.046, 0.047,
0.0012 and 0.037). Total 1000-h rotten fuel loading was higher
on mortality plots, but the difference was not significant
(P ¼ 0.065). In addition to higher downed woody fuels, mortality plots also had greater fuel-bed depth (P ¼ 0.02) and litter
depth (P ¼ 0.03) compared with no-mortality plots, but duff
depth did not differ between plot types (P ¼ 0.947; Table 2).
In all cases, pre-outbreak tree density was not a significant
covariate (P . 0.24) for any surface fuel loading variables.
Five years after the outbreak, mortality plots had lower
values in all canopy fuel loading variables compared with
no-mortality plots. Mortality plots had lower canopy foliage
biomass (P , 0.001), 1-h woody (P ¼ 0.008), 10-h woody
(P ¼ 0.001) and 100-h woody (P ¼ 0.004) fuel loads and both
total and available canopy fuel loading (P , 0.001) compared
with no-mortality plots (Table 3). Available canopy fuel loading
was on average two times lower in mortality plots than in
no-mortality plots. In addition, estimated canopy bulk density
averaged 53% lower (P ¼ 0.013) on mortality plots, but canopy
base height did not differ (P ¼ 0.399) between the two plot
types. Pre-outbreak tree density was a significant covariate
(P , 0.02) for all canopy fuel variables except 100-h canopy
fuels (P ¼ 0.339) and canopy base height (P ¼ 0.605).
Influence of a bark beetle outbreak on fuel loads
Int. J. Wildland Fire
309
Table 3. Five-years post-outbreak canopy fuel loadings in ponderosa pine stands
Adjusted means (s.e.) based on analysis of covariance using pre-outbreak tree density as a covariate. Percentage difference is
calculated between no-mortality and mortality plot averages. Asterisks (*) indicate significant differences (P # 0.05)
2
Foliage (kg m )
1-h (kg m2)
10-h (kg m2)
100-h (kg m2)
Total canopy fuel (kg m2)
Available canopy fuel (kg m2)
Canopy bulk density (kg m3)
Canopy base height (m)
No-mortality (n ¼ 30)
Mortality (n ¼ 30)
% difference
P
0.99 (0.07)
0.19 (0.02)
1.04 (0.08)
1.11 (0.14)
3.62 (0.34)
1.09 (0.08)
0.17 (0.019)
1.7 (0.4)
0.48 (0.07)
0.12 (0.02)
0.47 (0.08)
0.52 (0.14)
1.69 (0.33)
0.54 (0.07)
0.09 (0.018)
2.4 (0.4)
52%
37%
55%
53%
53%
50%
46%
þ41%
0.001*
0.022*
0.001*
0.001*
0.001*
0.001*
0.031*
0.817
Table 4. Five-years post-outbreak stand structural characteristics in ponderosa pine stands
Adjusted means (s.e.) are based on analysis of covariance using pre-outbreak tree density as a covariate. Percentage difference
is calculated between no-mortality and mortality plot averages. Asterisks (*) indicate significant differences (P # 0.05)
Quadratic mean diameter (cm)
Mean tree height (m)
Tree density of all species (stems ha1)
Density of ponderosa pine (stems ha1)
Density of other species (stems ha1)
Total basal area (m2 ha1)
Basal area of ponderosa pine (m2 ha1)
Basal area of other species (m2 ha1)
Stand density index
No-mortality (n ¼ 30)
Mortality (n ¼ 30)
% difference
P
28.7 (5.4)
10.4 (0.7)
466.8 (20.3)
332.7 (24.9)
134.1 (18.9)
29.8 (2.3)
16.6 (2.3)
13.1 (1.6)
208.8 (12.8)
29.9 (5.2)
9.2 (0.9)
273.4 (19.7)
139.1 (24.2)
134.3 (18.3)
12.4 (2.2)
6.4 (2.3)
5.9 (1.5)
91.6 (12.5)
þ4%
12%
41%
58%
þ,1%
58%
61%
55%
56%
0.981
0.282
0.014*
0.001*
0.102
0.001*
0.003*
0.077
0.001*
Post-bark beetle outbreak stand structure
Comparison of adjusted means based on analysis of covariance
revealed that mortality plots had lower total tree density
(P , 0.001) and density of ponderosa pine (P , 0.001) compared
with no-mortality plots 5 years after the outbreak (Table 4).
However, density of other species was not significantly different
between plot types (P ¼ 0.92). Total basal area and basal area of
ponderosa pine were also lower (P , 0.001) in mortality plots,
but basal area of other species was not significantly different
between plot types (P ¼ 0.07). On average, mortality plots lost
67% of the ponderosa pine and 45% of the total pre-outbreak
stems ha1. Trees that died during the outbreak had an average
quadratic mean diameter of 24.7 (1.26 s.e.) cm and ranged
from 10.2 to 73.4 cm. By 5 years after the outbreak, 48% of the
dead trees had fallen to the ground. The loss of ponderosa pine
from mortality plots resulted in a lower stand density index
(P , 0.001) compared with no-mortality plots. Pre-outbreak
tree density was a significant (P , 0.001) covariate for all
stand structure variables except basal area of ponderosa pine
(P ¼ 0.365) and quadratic mean diameter (P ¼ 0.724).
Discussion
Total surface woody fuel loadings were 2.5 times greater in plots
with bark beetle mortality compared with those without mortality 5 years after the outbreak. Increases in surface woody fuel
loadings were consistent across all time-lag size classes except
1000-h rotten fuels. The trend towards higher levels of 1000-h
rotten fuels on mortality plots (although not significant) suggests that some of the trees killed during the outbreak and deposited on the ground have not had enough time to decay. The
increase in surface woody fuel loadings 5 years after the outbreak follows the general pattern of downed woody fuel accumulations following bark beetle outbreaks for lodgepole pine,
Engelmann spruce (Picea engelmannii Parry ex Engelm.) and
Douglas-fir forests in some studies (Page and Jenkins 2007a;
Jenkins et al. 2008). However, Klutsch et al. (2009) found no
significant differences in downed woody fuel loadings in
lodgepole pine forests in Colorado 4 to 7 years after a mountain
pine beetle (Dendroctonus ponderosae Hopkins) outbreak.
Similarly, Simard et al. (2011) reported no significant increases
in woody fuel loading across a chronosequence in lodgepole
pine forests in Yellowstone National Park. It is likely that the
differences between our findings and these other studies are due
to a combination of differences in fall rates between lodgepole
pine and ponderosa pine and to differences between the causal
mortality agents. The model of Klutsch et al. (2009) projected
that significant differences in downed woody fuels would not
be expected until 80% of the snags had fallen. In contrast, we
found higher downed woody fuel loadings with only 48% of the
killed trees fallen. In our study, mortality was caused by a
combination of Ips plus Dendroctonus species (Hayes et al.
2008, Williams et al. 2008) compared with only mountain pine
beetle in Klutsch et al. (2009). Ips species typically attack
310
Int. J. Wildland Fire
smaller-diameter ponderosa pine compared with Dendroctonus
species (Breece et al. 2008).
A total of 20% of the mortality plots had accumulations of
1000-h fuels above recommended levels for dry coniferous
forests. Brown et al. (2003) estimated that accumulations of
1000-h fuels above 4.48 kg m2 exceeded levels needed to
maintain forest productivity and wildlife habitat and might lead
to difficulties in fire-line construction or increases in fire hazard
and soil heating, especially when logs are rotten. Five years post
outbreak, none of the no-mortality plots had 1000-h fuel loadings above 4.48 kg m2, but 20% of the mortality plots had loads
above this threshold. In addition, mortality plots still had 52%
of the snags standing, so we would expect that the percentage of
plots that are above the threshold reported by Brown et al.
(2003) will increase in the future. Continued increase in 1000-h
fuels with time since outbreak has been reported in lodgepole
pine stands affected by mountain pine beetle (Armour 1982;
Page and Jenkins 2007a; Klutsch et al. 2009) and for ponderosa
pine fall rates following wildfires in the South-west (Chambers
and Mast 2005).
Mortality plots were characterised by a litter layer twice as
deep as that of no-mortality plots, but had similar duff layer
depths. Increases in litter depths and no differences in duff
depths agree with reports following bark beetle outbreaks in
lodgepole pine forests in Colorado (Klutsch et al. 2009),
Yellowstone National Park (Simard et al. 2011) and in Utah
(Page and Jenkins 2007a) for time periods shortly after tree
mortality occurs. However, unlike studies in lodgepole pine
forests, we documented higher fuel-bed depths in mortality plots
than in no-mortality plots, which we attributed to a combination
of a greater accumulation of down woody material and higher
litter fall rates than in other studies.
Our findings also suggest that 5 years after the outbreak,
mortality plots had lower calculated foliage, 1-h, 10-h, 100-h,
total canopy, and both available canopy fuel loading and
canopy bulk density compared with no-mortality plots. The
lower canopy bulk density differs from the results of Page
and Jenkins (2007b) for recent bark beetle outbreaks in
lodgepole pine forests in central Idaho and in Utah, but is
consistent with reports for lodgepole pine forests in Yellowstone
National Park affected by mountain pine beetle (Simard et al.
2011). Declines in canopy fuel loading and bulk density can
be expected as needles of dead trees fall to the ground.
Contrary to our expectations that the mortality of small-diameter
trees attacked by Ips beetles would be associated with higher
canopy base heights, the difference between mortality and
no-mortality plots was not significant. We attributed this unexpected result to the fact that not just small trees (with crowns
low to the ground) were killed; the subsequent tree mortality
caused by Dendroctonus beetles likely killed larger trees and
thus counteracted the effect of the Ips beetles on canopy base
height.
Stands attacked by bark beetles had higher pre-outbreak tree
densities compared with stands that were not attacked. These
findings match those of Negrón et al. (2009) and Ganey and
Vojta (2011) who concluded that much of the initial tree
mortality during this outbreak occurred in areas that were prone
to drought stress (e.g. droughty soils, lower-elevation ecotones
and higher tree densities). Our results show that an average of
C. M. Hoffman et al.
45% of the total stems per hectare were killed in mortality plots.
After accounting for the pre-outbreak differences in tree density,
this pattern of tree mortality resulted in mortality plots having
lower total living tree density, density of ponderosa pine, living
basal area of all species combined and basal area of ponderosa
pine compared with no-mortality plots (Table 4). Negrón et al.
(2009) also reported decreased tree density, basal area and stand
density index in south-western pine forests following these
outbreaks.
A total of 48% of the trees that died within 5 years of the bark
beetle outbreak had already fallen. Past studies focussing on fall
rates following bark beetle outbreaks in ponderosa pine suggest
that trees begin to fall 3 years after outbreaks, with most trees
falling between 5 and 15 years after mortality (Keen 1955;
Schmid et al. 1985). In lodgepole pine forests killed by mountain pine beetle, Mitchell and Preisler (1998) and Harrington
(1996) both noted that smaller trees fell more quickly, particularly in stand openings. Negrón et al. (2009) hypothesised, and
our data support, that a reduction in snag longevity would occur
following the current outbreak owing to the primarily smaller
size classes of trees affected. Thus, our snag fall rates, which are
greater than those reported for other ponderosa pine sites, are
likely attributable to the mostly small trees affected during this
outbreak.
In summary, south-western ponderosa pine plots attacked
by bark beetles had higher tree density than plots that were
not attacked. Five years after the outbreak, when differences in
pre-outbreak tree density were accounted for, plots attacked by
bark beetles had lower tree densities, higher surface fuel
loadings and lower canopy fuel loadings, but similar canopy
base heights compared with plots that were not attacked by bark
beetles.
Conclusions
This study adds to the growing body of knowledge about the
role of drought and subsequent bark beetle outbreaks on tree
mortality. Based on US Forest Service aerial detection survey
data, an estimated ,7.6% of south-western forests and
woodlands were affected by bark beetles during droughty
conditions between 1997 and 2008 (Williams et al. 2010).
Evidence of drought-induced tree mortality is not restricted to
the south-western US, but has also been reported for other
forest types in western North America (van Mantgem et al.
2009) and in other parts of the world (Allen et al. 2010).
Projected future trends of warmer and drier climates (IPCC
2007) will likely continue to stress trees in many areas and
may alter life-history attributes and thus enhance population
success of some species of bark beetles such as the mountain
pine beetle (Bentz et al. 2010).
Given recent trends of increased tree mortality and projected
climate warming, we can expect that bark beetle outbreaks may
have increasing effects on fuel complexes and thus perhaps fire
hazard. However, research to date suggests that alterations to
fuels complexes are highly variable and depend on local site
factors as well as forest type, species of bark beetle involved and
time since outbreak (Bentz 2009). Teasing apart the importance
of these the factors and their interactions should be one goal of
future analyses.
Influence of a bark beetle outbreak on fuel loads
Acknowledgments
We thank Melissa Joy Fischer, Tania Begaye and Grace Hancock for field
data collection and entry, and Jesse Anderson and Kelly Williams for
creation of maps. We also thank the Apache–Sitgreaves, Coconino, Kaibab,
Prescott and Tonto National Forests for their support of this work. Funding
was provided by USDA Forest Service, Forest Health Monitoring, Evaluation Monitoring grant INT-F-07-01; USDA Forest Service Southwestern
Region, Forest Health; and USDA Forest Service, Rocky Mountain
Research Station.
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