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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, G02019, doi:10.1029/2005JG000101, 2006
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Changing temperatures influence suitability for modeled mountain
pine beetle (Dendroctonus ponderosae) outbreaks in the western
United States
Jeffrey A. Hicke,1 Jesse A. Logan,2 James Powell,3 and Dennis S. Ojima1
Received 8 September 2005; revised 17 January 2006; accepted 13 February 2006; published 16 June 2006.
[1] Insect outbreaks are significant disturbances in forests of the western United States,
with infestation comparable in area to fire. Outbreaks of mountain pine beetle
(Dendroctonus ponderosae Hopkins) require life cycles of one year with synchronous
emergence of adults from host trees at an appropriate time of year (termed ‘‘adaptive
seasonality’’) to overwhelm tree defenses. The annual course of temperature plays a major
role in governing life stage development and imposing synchrony on mountain pine beetle
populations. Here we apply a process-based model of adaptive seasonality across the
western United States using gridded daily temperatures from the Vegetation/Ecosystem
Modeling and Analysis Project (VEMAP) over the period 1895–2100. Historical
locations of modeled adaptive seasonality overlay much of the distribution of lodgepole
pine (Pinus contorta Douglas), a favored host, indicating that suitable temperatures for
outbreak occurred in areas of host availability. A range of suitable temperatures, both in
the mean and over an annual cycle, resulted in adaptive seasonality. Adaptive seasonality
typically occurred when mean annual temperatures were 3°–6°C, but also included
locations where mean temperatures were as low as 1°C or as high as 11°C, primarily as a
result of variability in winter temperatures. For most locations, years of adaptive
seasonality were uncommon during 1895–1993. We analyzed historical temperatures and
adaptive seasonality in more detail in three northern forest ecoprovinces. In the Northern
and Middle Rockies, areas of adaptive seasonality decreased at lower elevations and
increased at higher elevations during warmer periods, resulting in a movement upward in
elevation of adaptive seasonality. In contrast, the Cascade Mountains exhibited overall
declines in adaptive seasonality with higher temperatures regardless of elevation.
Projections of future warming (5°C in the western United States) resulted in substantial
reductions in the overall area of adaptive seasonality. At the highest elevations, predicted
warmer conditions will result in increases in the area of adaptive seasonality. Our findings
suggest that future climate change may alter forest ecosystems indirectly through
alteration of these important disturbances.
Citation: Hicke, J. A., J. A. Logan, J. Powell, and D. S. Ojima (2006), Changing temperatures influence suitability for modeled
mountain pine beetle (Dendroctonus ponderosae) outbreaks in the western United States, J. Geophys. Res., 111, G02019,
doi:10.1029/2005JG000101.
1. Introduction
[2] Insect outbreaks are important processes in forest
ecosystems, affecting succession, biogeochemical cycling,
and forest stand characteristics [e.g., Fleming et al., 2002;
McCullough et al., 1998]. Insect and pathogen infestations
influence an average annual area in the United States of
20.4 million ha, resulting in losses of $1.5 billion [Dale et
1
Natural Resource Ecology Laboratory, Colorado State University, Fort
Collins, Colorado, USA.
2
U.S. Department of Agriculture Forest Service, Logan, Utah, USA.
3
Department of Mathematics and Statistics, Utah State University,
Logan, Utah, USA.
Copyright 2006 by the American Geophysical Union.
0148-0227/06/2005JG000101$09.00
al., 2001]. The United States Department of Agriculture
Forest Service reported 1 – 5 million ha of insect- and
disease-caused tree mortality during 1997 – 2003 [USDA
Forest Service, 2004]. Forest area affected by insect outbreaks is comparable to that burned by fire. In the conterminous United States, fire has burned about 0.4– 2 million
ha in contemporary times [Houghton et al., 2000;
Leenhouts, 1998]. In Canada, areas of outbreak and fire
were similar in magnitude over the period 1920 – 1989
[Kurz and Apps, 1999]. In terms of volume, annual losses
during 1982– 1987 resulting from insect-caused tree mortality and growth loss in Canada averaged 52 million m3,
50% more than caused by fire [Hall and Moody, 1994].
[3] Recent studies have implicated climate, specifically
increasing temperature, as a major factor in insect outbreaks
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throughout western North America [Carroll et al., 2004;
Fastie et al., 1995; Logan and Powell, 2001]. Surface
temperatures are projected to warm in coming decades
[Intergovernmental Panel on Climate Change (IPCC),
2001]. These climate changes will likely influence the
distribution and outbreak frequency of many insect populations [Fleming, 2000; Logan et al., 2003; Williams and
Liebhold, 2002].
[4] Bark beetles (members of the family Scolytinae)
constitute a class of insects distributed throughout North
American forests that invade host tree species to feed and
reproduce, typically in dead or dying host trees. Most of
these beetles colonize twigs and branches without appreciable harm to the living tree, or they attack dead or dying
trees. However, a few species (such as mountain pine beetle,
Dendroctonus ponderosae Hopkins), termed primary or
aggressive bark beetles, attack the main stem of living trees,
killing the host tree in the process.
[5] Vast areas of bark beetle infestation in western North
America have occurred recently or are ongoing. In recent
decades, extensive outbreaks have occurred on the Kenai
Peninsula, Alaska (500,000 ha [Holsten et al., 2001]), in the
southeastern United States (3 million ha [USDA Forest
Service, 2004]), and across the western United States
(4.3 million ha [USDA Forest Service, 2004]). Projections
of outbreaks forecast large areas of damage. The Western
Bark Beetle Report [USDA Forest Service, 2002] states that
‘‘over the next 15 years, 8.5 million ha of western forests
are at high risk of experiencing significant tree mortality
caused by bark beetles.’’
[6] Mountain pine beetle affects the most area of any bark
beetle in western North America. The area attacked by
mountain pine beetle in Canada has risen to 7 million ha in
2004 [Ministry of Forests, 2005]. Forest area infested by
mountain pine beetle in the United States exceeded
1.5 million ha in the early 1980s. After a low of 150,000 ha
in the late 1990s, the area infested in the United States has
risen in recent years and is currently at almost 1 million ha
[USDA Forest Service, 2004].
[7] The majority of the mountain pine beetle life cycle is
spent as larvae feeding in the phloem tissue of host pine
trees [Amman et al., 1990]. This feeding activity eventually
girdles and kills successfully attacked trees (i.e., trees that
cannot resist infestation) [Amman and Cole, 1983; Furniss,
1997]. Most western pines are suitable hosts for this insect;
ponderosa pine, Pinus ponderosa Lawson, and lodgepole
pine, Pinus contorta Douglas, are among the major host
species [Amman et al., 1990]. After completing multiple
life stages over the course of 1 or 2 years, adult beetles
emerge from the host tree to attack new hosts [Amman et
al., 1990].
[8] Because host tree species have evolved chemical and
physical defenses, mountain pine beetles mass attack trees
to overcome these defenses [Amman et al., 1990; Raffa and
Berryman, 1987]. Synchronous emergence of adult beetles
from hosts is therefore required to maintain the outbreak
[Amman, 1985]. Furthermore, emergence should occur at an
appropriate time of year that avoids mortality from early
season freezing yet allows full ovipositional development
before lethal fall or winter temperatures [Amman, 1973].
Finally, outbreaks occur when the beetle life cycle completes in 1 year (termed ‘‘univoltine’’). This combination of
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univoltine conditions with synchronous emergence at an
appropriate time of season is termed ‘‘adaptive seasonality’’
[Logan and Powell, 2001].
[9] In contrast to many insect species, the mountain pine
beetle lacks an obligatory physiological state for synchronizing development (e.g., diapause) [Logan and Bentz,
1999; Powell and Logan, 2005]. Instead, life stage development is under direct temperature control. A processbased model of mountain pine beetle phenology has been
developed that incorporates the essential details of the
mountain pine beetle life cycle [Bentz et al., 1991; Logan
et al., 1995; Logan and Powell, 2001; Powell et al., 2000].
For a given annual cycle of daily temperatures, this model
estimates whether climate conditions result in adaptive
seasonality. The current model was parameterized using
developmental data from beetles collected from lodgepole
pine in central Idaho. The model, therefore, characterizes
the seasonality of populations found in the northern US
Rocky Mountains; implications of this assumption are
discussed in section 4. The model has successfully predicted outbreaks in lodgepole pine in central Idaho [Powell
and Logan, 2005].
[10] The objective of this study was to apply the adaptive
seasonality model to broad regions across the western
United States over long time periods. With a data set of
historical and projected temperatures developed for the
conterminous United States, we evaluated the spatial patterns of adaptive seasonality. In addition, we analyzed how
future climate change will affect adaptive seasonality and
therefore forest ecosystems.
2. Methods
2.1. Adaptive Seasonality Model
[11] Adaptive seasonality modeling simulates the mountain pine beetle life cycle. The model has been discussed
in detail in previous studies [Bentz et al., 1991; Logan and
Amman, 1986; Logan et al., 1995; Logan and Powell,
2001]; here we briefly describe the method used. The
basic modeling framework considers that the developmental rate, r(T), is the inverse of the time required to
complete a particular life stage at temperature T. Life stage
developmental rates (rj) were determined from laboratory
measurements for Idaho beetle populations in lodgepole
pine bolts. The functional forms and parameters for
developmental rates of all eight life stages of the mountain
pine beetle are given by Logan and Amman [1986] and
Bentz et al. [1991]. The developmental index, aj, in stage j
is the fraction of the jth life stage completed at any
particular time by the median individual in the population,
and is related to the developmental rate by a differential
equation:
d
aj ðt Þ ¼ rj ½T ðt Þ:
dt
ð1Þ
Life stage j begins at time tj1, which is the time of
completion of the previous life stage, and defines the initial
condition of equation (2),
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aj t ¼ tj1 ¼ 0:
ð2Þ
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By integrating equation (1) from this initial condition, we
find the developmental index, aj, at time t,
aj ðt Þ ¼
Zt
0
0
rj ½T ðt Þdt :
ð3Þ
tj1
Life stage j finishes at time tj, at which aj = 1,
aj t ¼ tj ¼ 1:
ð4Þ
By evaluating equation (3) numerically and finding the
earliest t for which the cumulative development is 1, it is
possible to determine the developmental time spent in stage
j in a variable temperature environment. Logan and Powell
[2001] illustrate these concepts graphically and with
additional detail.
[12] Beginning with an egg laid on an arbitrary start date
and progressing though the other life stages, the adaptive
seasonality model numerically integrates equation (3) using
annual temperature cycles, thereby computing the time
required to complete each life stage. Each initial oviposition
date is thus associated with a particular emergence date; the
output of the model is the total number of days required to
complete all life stages, starting on a given oviposition date.
The model is iterated from generation to generation, resulting in a sequence of oviposition dates. As discussed by
Powell and Logan [2005], a natural consequence of this
model structure is that every dynamic sequence of ovipositional dates is attracted to the same life cycle regardless of
initial oviposition date. If this cycle is exactly 1 year long
(and therefore consists of a single, fixed point), the population is univoltine, and the univoltine fixed point attracts
and therefore synchronizes the emergence of the entire
beetle population. Multiple generations of beetles are modeled for each annual cycle of temperature to evaluate the
convergence of the results.
[13] Three conditions must be met for adaptive seasonality: Development must complete in a single year, populations must be able to synchronize their emergence, and
emergence must occur at a reasonable time of year. The first
two conditions are satisfied only if a univoltine fixed point
exists, as described above. The third condition ensures that
eggs are laid before vulnerability to freezing becomes an
issue, and guarantees that cold-hardened larval instars occur
during the coldest season. This condition is checked by
determining if the univoltine fixed point appears in an
appropriate seasonal window (July through August). It is
important to recognize that temperatures may be too high to
support adaptive seasonality as well as too low [Logan and
Powell, 2001]; as shown later, this turns out to be an issue
with projected climate change.
[14] Logan and Powell [2004] and Powell and Logan
[2005] used this model to explore climatic control of an
ongoing outbreak of mountain pine beetle in Stanley, Idaho.
Modeling results since the summer of 1995– 1996 have
predicted univoltine, synchronous life cycles for mountain
pine beetles (i.e., adaptive seasonality). These predictions
have been accompanied by an rapid increase in outbreak
area as revealed by aerial detection surveys [Logan and
Powell, 2004]. The last mountain pine beetle outbreak in the
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Stanley, ID area occurred in the late 1920s to early 1930,
also an unusually warm period [Logan and Powell, 2001].
2.2. VEMAP Temperatures
[15] The adaptive seasonality model is driven by hourly
temperatures through the course of a year. Daily minimum
and maximum temperatures are sufficient for deriving a
diurnal temperature cycle, which is then used in the model.
Few spatially extensive records of daily temperatures are
available at sufficiently fine temporal resolution to drive the
model. In this study, we used temperatures from the
Vegetation/Ecosystem Modeling and Analysis Project
(VEMAP [Kittel et al., 2004]).
[16] VEMAP temperatures are based on measurements
for the period 1895 – 1993; 1993 was the last year for which
observations were available for the development of the data
set. Observational input data sets to VEMAP include the
United States Historical Climate Network (USHCN), cooperative networks, and the SNOTEL (snowpack telemetry)
data set. The USHCN data were selected for their high
quality (i.e., minimal biases). However, this data set is
limited in spatial extent, and as a result the cooperative
network and SNOTEL data sets were utilized to fill in
spatial and temporal gaps in the VEMAP data set. The
SNOTEL data in particular were useful for specifying
higher elevation behavior. VEMAP temperatures were generated with the goal of completeness in space and time and
as a result may be limited in their ability to detect long-term
trends [Kittel et al., 2004].
[17] General circulation model climate scenarios were
downscaled to the VEMAP grid for the period 1994 –
2100 [VEMAP Members, 1995]. These scenarios resulted
from runs using the Canadian Climate Center (CGCM1;
3.75° by 3.75°, 19 vertical levels) and the United Kingdom
Hadley Centre (HADCM2; 2.5° by 3.75°, 10 vertical levels)
models. Both models included increasing atmospheric CO2
and sulfate concentrations. Measured concentrations were
used until 1993; afterward, both atmospheric constituents
increased at an idealized rate of 1% per year. This scenario
lies near the middle of the range of emission scenarios used
in the 2001 Intergovernmental Panel on Climate Change
(IPCC) assessment in terms of CO2 concentrations in 2100
[IPCC, 2001]. Model output was interpolated to the
VEMAP grid and adjusted for high-resolution topography.
For this study, we report results using the CCC model.
Temperatures in 2100 predicted by the HADCM2 in the
western United States were 1.5°C cooler than those from the
CCC model, but similar qualitative results of adaptive
seasonality were obtained with the HADCM2 model. Both
models tended to predict a global mean temperature increase
slightly larger than the mean of a variety of models used in
the most recent IPCC assessment [IPCC, 2001].
[18] The spatial resolution of VEMAP data sets is 0.5°.
Although relatively coarse, this resolution accounts for
some topographic changes that are important when assessing temperature variability across the mountainous western
United States. Topographic effects on temperature were
accounted for through the use of a model that includes
elevation for interpolation of climate variables (ParameterRegression on Independent Slopes [Daly et al., 1997]).
[19] Hourly temperatures are required by the adaptive
seasonality model. Temporal downscaling of monthly
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mountain pine beetle outbreaks [USDA Forest Service,
2004]: the Northern Rockies (total area is 10 million ha),
the Middle Rockies (21 million ha), and the Cascades
(14 million ha). For each year, annual temperatures from
all VEMAP grid cells within each ecoprovince were used to
compute the mean annual temperature. Although daily
temperatures drove the adaptive seasonality model, annual
temperatures were plotted to convey long-term variability
and trends. Area of adaptive seasonality within the ecoprovince was calculated as the sum of the area of VEMAP grid
cells with adaptive seasonality for the given year.
[22] Grid cells within each ecoprovince were divided into
low and high elevation bins to allow temporal analysis of
differences in temperature and adaptive seasonality behavior across an elevation gradient. We selected median elevations within each ecoprovince to separate low and high
bins; these elevations were 1300 m (Northern Rockies),
2000 m (Middle Rockies), and 1000 m (Cascade).
3. Results
Figure 1. Locations (in black) that had at least 1 year of
adaptive seasonality from 1895– 1993. White lines indicate
distribution of lodgepole pine, a favored host of mountain
pine beetle; note the generally good agreement with areas of
adaptive seasonality. Thick white circles at 47.25°N,
121.75°W (Washington), 38.75°N, 105.25°W (Colorado),
and 47.75°N, 116.25°W (Idaho) indicate locations shown in
Figure 4. Three of Bailey’s ecoprovinces (Cascade, Northern Rockies, and Middle Rockies) displayed in Figure 5 are
indicated with hatching. Also, 43.9°N, 114.8°W indicates
location of mountain pine beetle population that was used to
parameterize the model. Spatial resolution of grid cells is
0.5°. State names are indicated by abbreviations.
means to hourly values was accomplished in two steps.
First, the VEMAP project utilized a modified version of
WGEN to generate daily minimum and maximum values
[Kittel et al., 2004]. WGEN parameterizes the means and
variances of values using daily and monthly observations
[Parlange and Katz, 2000]; VEMAP used 526 high-quality
stations during the period 1930 – 1989 to develop these
parameterizations [Kittel et al., 2004].
[20] Hourly temperatures for use in the adaptive seasonality model were calculated from VEMAP daily minimum
and maximum temperature using a sawtooth pattern. The
sawtooth pattern was found to predict observed voltinism
better than a variety of other methods, including sinusoidal,
sinusoidal during the day with an exponential lag at night,
square, and Newton’s Law of Cooling with a radiant flux
term (p < 0.01 using a chi-squared analysis) [Hanks et al.,
2001]. Minimum temperatures were assumed to occur at
sunrise, and maximum temperatures were assumed to occur
3 hours before sunset. Temperatures were linearly interpolated between these times and values.
2.3. Ecoprovince Results
[21] Average temperatures and area of adaptive seasonality were computed for three of Bailey’s ecoprovinces
[Bailey and Hogg, 1986] that have experienced extensive
3.1. Presence of Adaptive Seasonality
[23] To indicate locations across the western United
States that had adaptive seasonality, we mapped cells for
which we estimated at least one year of adaptive seasonality
during the historical time period (1895 –1993) (Figure 1).
Thermally suitable locations for outbreak occurred across
the northern and central mountain ranges of the western
United States, including the Cascades, the Sierra Nevada,
and the Rocky Mountains. We also plotted the distribution
of a favored host, lodgepole pine [Little, 1971]. The
modeled adaptive seasonality occurred in most regions
where lodgepole pine is found, even in the very localized
region around the Black Hills of South Dakota (44°N,
103°W). In addition, the modeled locations of climate
suitability in the United States was in good agreement with
a map of the probable distribution of mountain pine beetle
[Amman et al., 1990].
[24] Notable differences compared with the lodgepole
pine distribution occurred in the northwest corner of Wyoming (44°N, 104°W) and in the Southern Rockies of Colorado (40°N, 107°W), where modeled adaptive seasonality
occurred at lower elevations, but not at the elevations of
lodgepole pine. Outbreaks have occurred or are ongoing in
lodgepole pine in both locations, however [Despain, 1990;
USDA Forest Service, 2003]. Another known outbreak area
where the model did not predict adaptive seasonality is in
ponderosa pine in the mountains of Arizona (35°N, 111°W)
[Furniss and Carolin, 1977]. The model predicted adaptive
seasonality in regions where lodgepole pine is not found,
most significantly in southern Utah (38°N, 112°N), where
ponderosa pine is found, and northeastern Nevada (41°N,
116°W). Modeled adaptive seasonality differed in some
details from the map of potential mountain pine beetle
range as defined by Amman et al. [1990], most notably in
Arizona and New Mexico.
[25] The total number of years of adaptive seasonality
during 1895– 1993 varied considerably, ranging from 1 to
80. No spatial pattern was apparent across the western
United States (Figure 2a) since both elevation and latitude/longitude played roles in determining adaptive seasonality. The cumulative distribution of years of adaptive
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Figure 2. (a) Number of years of adaptive seasonality during the 99 years between 1895 and 1993.
(b) Cumulative distribution of number of years of adaptive seasonality. A large number of locations,
scattered throughout the region, had only a few years of adaptive seasonality, implying that the
temperatures conditions were usually unfavorable for outbreak.
seasonality revealed that the majority of years within most
grid cells were climatically unsuitable for mountain pine
beetle outbreak. For example, 50% of the grid cells had
<20 years when temperatures produced adaptive seasonality,
and 80% of the grid cells had <55 years, just over half of the
total number of years between 1895 and 1993.
3.2. Variability in Temperatures Resulting in
Adaptive Seasonality
[26] A range of annual cycles of temperature resulted in
adaptive seasonality. Plotting the distribution of mean
annual temperatures of years and locations revealed that
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Figure 3. Frequency distribution of mean annual temperatures for years and grid cells with adaptive seasonality.
Although the peak is around 5°C, the range extends from
1° – 11°C.
most cases of adaptive seasonality occurred when mean
temperatures were 4° – 5°C (Figure 3). However, years
when mean temperatures were as low as 1°C or as high
as 11°C also sometimes resulted in adaptive seasonality.
The slight increase in the distribution at 9° – 10°C was
associated with locations in northwestern Washington,
where warmer winter conditions resulted in adaptive seasonality (see Figure 4).
[27] The mean annual cycle of temperatures at three
locations that experienced adaptive seasonality highlights
this variability (Figure 4). Washington temperatures were
relatively high in winter. In contrast, Colorado winter
temperatures were lower by 9°C, though summer temperatures were similar. Idaho temperatures were intermediate in
winter but higher in summer by 2°C. Substantial variability
in the annual cycle of temperatures that resulted in adaptive
seasonality also occurred within years at one location (not
shown).
3.3. Historical Behavior for Three Ecoprovinces
[28] Adaptive seasonality during 1895 – 1993 occurred in
many locations within the three selected ecoprovinces
(Cascades, Northern Rockies, Middle Rockies) (Figure 1).
Mean temperatures exhibited similar behavior across the
ecoregions and elevations (Figure 5). Warm periods occurred between the late 1930s and the early 1940s, with
recent years showing large increases in temperature (except
for higher elevation Middle Rockies). Although longer-term
temperature patterns in the VEMAP data set may be
contaminated by the inclusion of lower quality stations,
comparisons with high quality USHCN stations revealed
similar patterns (http://www.nrel.colostate.edu/jhicke/
climate_data). The area of adaptive seasonality was negatively correlated with temperature at lower elevations in the
Rockies ecoprovinces (Figures 5a, 5c, 6a, and 6c), but
positively correlated at higher elevations (Figures 5b, 5d,
6b, and 6d). In the Cascades ecoprovince, area was negatively correlated with temperature at both lower and higher
elevations (Figures 5e, 5f, 6e, and 6f).
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3.4. Effects of Future Warming
[29] The scenario of future climate change by the CCC
general circulation model and downscaled to the VEMAP
grid predicts warming in the western United States of 5°C
by 2100 (Figure 7, bottom). We found that these increases
will reduce the overall area of adaptive seasonality as
temperatures become too high in many areas to support
adaptive seasonality. By the end of the time period (2100),
we estimated that only 1 million ha in the western United
States will have thermally suitable conditions for outbreak,
compared to 20– 30 million ha historically.
[30] Binning temperatures and area by elevation revealed
different patterns. Over the western United States, reductions in area of adaptive seasonality at the lowest elevations
(0– 1500 m; Figure 7) occurred after 1980 and will continue into the future. Between 1500 and 2500 m, we
forecast that the decline will begin about 2025. For the
highest two elevation bins, increases will occur initially
(1994 forward) as temperatures increase. However, the area
of adaptive seasonality will then decrease after 2040 in the
2500- to 3000-m bin, and after 2060 for the highest
elevation bin. Our results indicated that the declines in
area for the upper three elevation bins will occur when
mean annual temperatures were 5° – 6°C. The mean annual
temperature of locations of adaptive seasonality within the
lowest elevation bin was historically about 7°C, and
therefore projected temperature increases after 1994 (in
fact, slightly before) will drive these areas out of adaptive
seasonality immediately.
4. Discussion
4.1. Spatiotemporal Patterns of Adaptive Seasonality
[31] The overlapping adaptive seasonality and lodgepole
pine distributions imply that mountain pine beetle and
Figure 4. Mean annual cycle of temperature for the three
locations, Washington (WA; elevation is 515 m; solid
curve), Idaho (ID; 921 m; dashed-dotted curve), and
Colorado (CO; 2662 m; dashed curve), circled in Figure 1.
Only years of adaptive seasonality were included. Note the
wide range of annual cycles, primarily resulting from
variability in winter temperatures, associated with adaptive
seasonality.
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Figure 5. Anomalies of temperature (black) and area of adaptive seasonality (gray) relative to the
1895 –1993 means for three ecoprovinces. (left) Lower elevations for each ecoprovince. (right) Higher
elevations. Thick lines represent time series smoothed with a 20-point Lowess smoother.
lodgepole pine occupy the same bioclimatic envelope.
Persistence of lodgepole pine across a landscape requires
periodic disturbance [Burns and Honkala, 1990; Clements,
1910; Despain, 1990; Pfister and Cole, 1985]. Bark beetles
act as disturbances to these forests, and at various times
following outbreak, can enhance the potential for fire
[Fleming et al., 2002; Stuart et al., 1989; Turner et al.,
1999], another forest disturbance. Outbreak and fire create
conditions favorable for lodgepole pine regeneration instead
of other species [Burns and Honkala, 1990].
[32] The variability in mean annual temperatures and
mean annual cycles of temperature that led to adaptive
seasonality primarily resulted from a range of winter temperatures. During these cold conditions, life stage development is slow, and thus sensitivity to temperature variability
is reduced. From late spring throughfall, temperatures were
more similar among the sites analyzed (Figure 4). The range
of mean conditions resulting in adaptive seasonality demonstrates the necessity of process-based models of insect
phenology for assessing climate effects on outbreaks. A
simple analysis of mean annual temperature, while instructive (as in some of the figures in this paper), may miss some
important dynamics.
[33] Whether adaptive seasonality increased or decreased
in response to interannual variability in warming depended
on the current thermal regime. At warmer locations (lower
elevations or coastal mountains), we found that contemporary temperatures were suitable for adaptive seasonality,
whereas higher elevations or locations in the interior of the
continent were too cold. Thus the higher mean temperatures
of the Cascades supported adaptive seasonality at all elevations, and increases in temperature reduced the area of
adaptive seasonality. In contrast, in the colder Rockies, the
locations of adaptive seasonality moved upslope in response
to warming.
[34] Uncertainty about future temperature projections
may lead to increased or decreased warming relative to
the CGCM1 results we used here. The response of adaptive
seasonality to this range can be gauged from our findings.
Additional warming beyond that predicted by the CGCM1
model will result in reductions to the area of adaptive
seasonality at the highest elevations, where we predict
suitable conditions will still occur in 2100. Decreased future
warming will lead to greater area of adaptive seasonality
(i.e., lower reductions in area). For instance, the temperature
increase predicted by HADCM2 of 3.5°C instead of 5°C
would result in about 3 million ha of adaptive seasonality in
2100, corresponding to the year 2075 in the CGCM1 runs.
[35] Our predicted sensitivity of outbreaks to climate
change are in agreement with a large body of existing
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Figure 6. Anomalies of temperature and area of adaptive seasonality relative to the 1895 – 1993 means
for three ecoprovinces (same as plotted in Figure 5). (left) Lower elevations for each ecoprovince. (right)
Higher elevations. In the lower elevation Rocky Mountain ecoprovinces as well as in the Cascades,
temperatures and adaptive seasonality are negatively correlated. In the higher elevation Rocky
Mountains, the correlation is positive. Correlations are significant (p < 0.05) when r = 0.17.
literature on a number of insect species and forest types
[e.g., Bale et al., 2002; Carroll et al., 2004; Fastie et al.,
1995; Fleming, 2000]. Specifically, Williams and Leibhold
[2002] developed a statistical model that related climate
variables to mountain pine beetle outbreak in the Pacific
Northwest to explore changes in MPB range in response to
prescribed step changes in temperature. These authors found
a positive correlation between temperature and MPB distribution, and that as temperatures increased by 2°, 4°, and
8°C, the MPB range decreased in area and moved upward in
elevation. Our results using a process-based model with
transient climate scenarios over the entire western United
States confirmed these findings.
4.2. Modeling Considerations
[36] Different populations of mountain pine beetle have
different developmental rates [Bentz et al., 2001]. More
southern populations are adapted to higher temperatures
through reduced developmental rates, thus requiring more
thermal input to complete the life cycle. Our study utilized
rates from Idaho populations, and this assumption may have
led to the unsuccessful prediction of adaptive seasonality in
areas of known outbreak in some southern areas. Related to
this variability, we assumed that mountain pine beetles do
not evolve in response to warmer conditions over the time
period of this study.
[37] Two sources of temperature variability were not
represented here. Differences between air and phloem
temperatures will influence beetle populations; our results
here used air temperatures. A comparison of Figure 1 with a
model run using a conversion to phloem temperatures
resulted in little change in the overall regional patterns. In
addition, spatial variability in microclimate within a 0.5°
grid cell was not captured by the VEMAP temperatures, and
therefore some areas actually suitable for outbreak within a
grid cell could not be accurately modeled.
[38] As with any model, the predictions of the adaptive
seasonality model do not match expectations over the full
range of possibilities (e.g., lack of adaptive seasonality in
areas of known outbreaks). In addition to the variability in
insect populations and temperatures discussed above, the
model framework may be too constraining. The adaptive
seasonality model predicts that annual temperatures are
either suitable or not for outbreak for the median individual
in a MPB population. This binomial modeling construct
ignores variability in developmental rates and emergence
(and thus timing of attack) across the population that occurs
in reality. Further research will apply a process model that
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Figure 7. Mean annual temperature (black) and area of adaptive seasonality (gray) from 1895– 2100
(bottom) for all areas and (top four plots) four elevation bands in the western United States: 0 –1500 m,
1500 –2500 m, 2500– 3000 m, 3000 – 3500 m. Note changes in y axis ranges for area of adaptive
seasonality. Temperatures were computed using only grid cells with at least 1 year of adaptive
seasonality. The shift from historical to projected temperatures in 1993 is indicated by the vertical dotted
line. Projected warming leads to reduced area of adaptive seasonality except at the highest elevations,
which have increases.
considers this population variability, and will investigate
other sources of temperature to improve the predictions of
temperature effects on population levels.
4.3. Additional Factors to Predicting Outbreaks
[39] In this paper we discuss adaptive seasonality, or the
suitability of temperatures for mountain pine beetle outbreaks. However, adaptive seasonality is only one of several
conditions that need to be met for an outbreak to actually
occur. Other contributors include stand characteristics and
other weather conditions. Presence of host species is an
obvious requirement. Favored hosts of mountain pine beetle
include lodgepole and ponderosa pine, and to a lesser extent
other pine species. Stand conditions that influence tree vigor
and therefore resistance to attack also contribute to susceptibility [Amman and McGregor, 1985]. Shore and Safranyik
[1992] identified three stand characteristics that increase
susceptibility of a stand to outbreak: sufficiently large basal
area of host species, stand age >60 –80 years, and interme-
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diate stem density. The current structure of western conifer
forests, with a higher proportion of older stands [Ferry et
al., 1995; Taylor and Carroll, 2004], may play a major role
in driving recent infestations. Other weather variables used
to assess outbreak potential include minimum winter temperatures that may cause beetle mortality, August temperatures associated with beetle flight, and drought indices that
may reflect lowered tree resistance through reduced soil
moisture [e.g., Carroll et al., 2004].
[40] Several other bark beetles are present at the southern
edge of the mountain pine beetle distribution, such as the
roundheaded pine beetle (Dendroctonus adjunctus Blandford), the larger Mexican pine beetle (Dendroctonus mexicanus Dietz), and the southern pine beetle (Dendroctonus
frontalis Zimmermann). In addition, southern populations of
MPB have lower developmental rates and thus are better
adapted to warmer conditions. These MPB populations and
different Dendroctonus species may expand their range
northward to fill locations that we predict will become
unsuitable for mountain pine beetle.
4.4. Implications for Forest Ecosystems
[41] At lower elevations, the future reduction in adaptive
seasonality area suggested by this study will have important
implications for forest ecosystems. Removal of a significant
disturbance may shift more stands from intermediate successional stages to climax stages. For example, in the
Middle Rockies, the reliance of lodgepole pine on disturbance for regeneration implies that decreased outbreak
frequency resulting from climate change (in the absence
of changes in fire regime) may result in shifts to subalpine
fir [Despain, 1990]. Changing disturbance regimes and
increases in stand ages will impact biogeochemical cycling,
including net primary production [e.g., Hicke et al., 2003]
and net carbon fluxes [e.g., Kurz and Apps, 1999].
Responses of fire and outbreak disturbance to climate
change and their interaction may substantially alter forest
ecosystems. For instance, warming may lead to increased
fire frequency, which in turn may lead to reduced stand ages
across the landscape. Since older stands are more susceptible to mountain pine beetle outbreak [Shore and Safranyik,
1992], this reduced stand age may result in decreases in
outbreak.
[42] Our predictions of increased area of adaptive seasonality at higher elevations in coming decades will increase stresses to high-elevation pine ecosystems. In
particular, whitebark pine (Pinus albicaulis Engelm.) grows
at high elevations in the western United States, and has a
life history that makes its populations particularly susceptible to elimination by bark beetles. Although mountain pine
beetle outbreaks have occurred in these forests in the past,
these were episodic events, affecting whitebark pine stands
for limited time periods (e.g., the warm periods in 1930 –
1940 seen in Figure 5 [Logan and Powell, 2001]). This
contrasts with lodgepole pine, which lives in many locations
in thermal conditions suitable for outbreak (Figure 2).
Whitebark pine appears to be particularly vulnerable to
mountain pine beetle depredation for a variety of reasons.
The relatively thick phloem provides an improved food
resource as compared to lodgepole pine [Amman, 1982].
Additionally, whitebark pine may be less well protected by
chemical defenses than lodgepole pine, perhaps in response
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to a lack of significant adaptive pressure from bark beetles
(J. A. Logan and J. A. Powell, unpublished data, 2005). Our
model estimates of increased adaptive seasonality at high
elevations are particularly alarming for this sensitive ecosystem [Logan and Powell, 2001]. In recent years, widespread outbreaks of mountain pine beetles have occurred
across the northern U.S. Rocky Mountains concurrent with
an observed unprecedented warming [Powell and Logan,
2005]. If this trend continues, these communities, already at
risk from widespread mortality from an introduced pathogen (white pine blister rust), will face an increasingly
uncertain future [Tomback, 2002]. In particular, the substantial effort that has gone into establishing blister rust
resistant strains of whitebark pine could be sidetracked by
mountain pine beetle outbreaks that are not affected by
disease resistance.
[43] Our results forecast reduced area of adaptive seasonality in the western United States as a result of higher
projected temperatures. However, the distribution of lodgepole pine extends farther north into British Columbia. The
northern range limit of mountain pine beetle currently is
limited by climate, not host availability (i.e., lodgepole pine
exists well north of the beetle range limit) [Carroll et al.,
2004]. Recent warming has been implicated as responsible
for the dramatic expansion in outbreak area in British
Columbia, with continued northward migration forecast
with increasing temperature [Carroll et al., 2004]. Logan
and Powell [2001] hypothesized that with sufficient warming, range expansion of mountain pine beetle in the Canadian Rockies may lead to invasion into stands of a possible
host, jack pine (Pinus banksiana Lamb.), with widespread
distribution across Canada.
5. Summary and Conclusions
[44] A process-based model that predicts whether input
temperatures are suitable for outbreaks of mountain pine
beetle was run with gridded temperatures across the western United States. Temperatures and adaptive seasonality
were analyzed for historical (1895 – 1993) and projected
(1994– 2100) time periods as defined in the VEMAP data
set. Temporally, adaptive seasonality occurred less frequently rather than more frequently across the region, with
80% of the grid cells with any adaptive seasonality having
fewer than 55 years of climate suitability (out of 99). We
found that adaptive seasonality occurred across a range of
mean annual temperatures and mean annual cycles of
temperature. This range resulted from variability in winter
temperatures, when developmental rates are low and therefore life stage development is less sensitive to temperature
variability.
[45] Model estimates of adaptive seasonality resulted in
areas that matched the distribution of a favored host, lodgepole pine, as well as agreeing with a potential range map of
mountain pine beetle. This finding was obtained with the
parameterization of the model using data from central
Idaho, and is an indication that developmental rate estimates
for this population are probably characteristic for most
mountain pine beetle populations occurring in the U.S.
distribution of lodgepole pine.
[46] From the mid-1970s to the early 1990s, historical
patterns in the Northern and Middle Rockies ecoprovinces
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revealed an upward shift of adaptive seasonality to higher
elevations in periods of higher temperatures with a concurrent decrease in adaptive seasonality at lower elevations.
These shifts are associated with an increase in outbreak
reported in novel environments (i.e., high-elevation whitebark pine). In contrast, in the warmer Cascades, increases in
temperature resulted in decreases in adaptive seasonality at
all elevations.
[47] Our modeled results suggest that projected warming
across the western United States will lead to a decrease in
overall modeled area of adaptive seasonality. At higher
elevations, we estimate that increases in the area of adaptive
seasonality will occur early in the twenty-first century, but
as temperatures will continue to rise, conditions will become too warm to support mountain pine beetle outbreaks
and area will decline.
[48] Adaptive seasonality modeling is a first step toward
process-based predictions of mountain pine beetle outbreak.
Additional processes needed to predict outbreaks include
mapping host species stand characteristics, including distribution, basal area, age, and density, as well as including
other effects of climate on insect populations. Migration of
other populations of MPB with different developmental
rates [Bentz et al., 2001] or other bark beetle species in
response to warming may fill in where we predict reductions in outbreaks will occur. Future research to incorporate
these processes as well as to develop process-based modeling of additional bark beetle species will allow us to better
understand the effects of climate change on forests in
western North America.
[49] Consequences of changing outbreak regimes range
from effects at local scales (such as bark beetles as invasive
species in high elevation pine ecosystems [Logan and
Powell, 2001]) to effects at regional scales (such as the
northward migration of beetles into jack pine ecosystems
[Carroll et al., 2004]) to effects at global scales (such as
biogeochemical cycling [Kurz and Apps, 1999]). Improved
understanding of the role of climate in driving insect outbreaks will enhance our ability to forecast future forest
responses to projected climate change.
[50] Acknowledgments. Thanks are owed to Linda Joyce for helpful
discussions and to Lee Casuto for providing programming support. We
appreciate the thoughtful and constructive suggestions of two anonymous
reviewers, which improved the manuscript. This work was funded by the
USDA Forest Service Cooperative Agreement 03-CS-11222033-315, the
USGS Western Mountain Initiative (USGS Cooperative Agreement
04CRAG0004/4004CS0001), NASA grant NNG04GH63G, and NSF grant
DMS-0077663.
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