Global Pyrogeography: the Current and Future
Distribution of Wildfire
Meg A. Krawchuk1, Max A. Moritz1*, Marc-Andre´ Parisien1,2, Jeff Van Dorn3, Katharine Hayhoe3,4
1Department of Environmental Science, Policy and Management, University of California, Berkeley, California, United States of America, 2Natural Resources Canada,
Canadian Forest Service, Edmonton, Alberta, Canada, 3ATMOS Research and Consulting, Lubbock, Texas, United States of America, 4Department of Geosciences, Texas
Tech University, Lubbock, Texas, United States of America
Abstract
Climate change is expected to alter the geographic distribution of wildfire, a complex abiotic process that responds to a
variety of spatial and environmental gradients. How future climate change may alter global wildfire activity, however, is still
largely unknown. As a first step to quantifying potential change in global wildfire, we present a multivariate quantification
of environmental drivers for the observed, current distribution of vegetation fires using statistical models of the relationship
between fire activity and resources to burn, climate conditions, human influence, and lightning flash rates at a coarse
spatiotemporal resolution (100 km, over one decade). We then demonstrate how these statistical models can be used to
project future changes in global fire patterns, highlighting regional hotspots of change in fire probabilities under future
climate conditions as simulated by a global climate model. Based on current conditions, our results illustrate how the
availability of resources to burn and climate conditions conducive to combustion jointly determine why some parts of the
world are fire-prone and others are fire-free. In contrast to any expectation that global warming should necessarily result in
more fire, we find that regional increases in fire probabilities may be counter-balanced by decreases at other locations, due
to the interplay of temperature and precipitation variables. Despite this net balance, our models predict substantial invasion
and retreat of fire across large portions of the globe. These changes could have important effects on terrestrial ecosystems
since alteration in fire activity may occur quite rapidly, generating ever more complex environmental challenges for species
dispersing and adjusting to new climate conditions. Our findings highlight the potential for widespread impacts of climate
change on wildfire, suggesting severely altered fire regimes and the need for more explicit inclusion of fire in research on
global vegetation-climate change dynamics and conservation planning.
Citation: Krawchuk MA, Moritz MA, Parisien M-A, Van Dorn J, Hayhoe K (2009) Global Pyrogeography: the Current and Future Distribution of Wildfire. PLoS
ONE 4(4): e5102. doi:10.1371/journal.pone.0005102
Editor: Jerome Chave, Centre National de la Recherche Scientifique, France
Received December 10, 2008; Accepted February 25, 2009; Published April 8, 2009
Copyright:
2009 Krawchuk et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding provided by The Nature Conservancy’s Global Fire Initiative and Natural Sciences and Engineering Research Council of Canada. The funders
had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: mmoritz@nature.berkeley.edu
Introduction
Wildfire is an ecological disturbance process that has a
heterogeneous global distribution controlled by the coincidence
of three basic requirements: vegetative resources to burn,
environmental conditions that promote combustion, and ignitions.
While the physical process of combustion is theoretically simple,
understanding the relative influence of biotic and abiotic controls
on observed, modern fire regimes is an ongoing focus in ecological
research, nuanced by the role of humans who are changing
landscapes to be more or less flammable, as well as lighting and
extinguishing fires [1–3]. Interest in fire research has become
global and interdisciplinary due to influences, interactions, and
feedbacks among fire, terrestrial, and atmospheric systems in the
context of human health [4], climate dynamics [5], and policy
adaptation [6].
Recent work has begun to synthesize common trends in
environmental influence on fire across broadly different locations
[7,8], but our comprehension of overarching biophysical controls
on global fire activity is still limited. The collection of fire data by
remote sensing provides an archive from which to examine global
patterns of wildfire, such as differences between areas of the planet
where fire occurs and those where it does not. The first cohort of
global fire studies focused on validation and translation of remotely
sensed fire products to area burned [9,10], global carbon emissions
from fire [5], and how seasonal variation in fire relates to ocean-
atmosphere cycles [11–13]. An initial characterization of the
global fire environment by Dwyer et al. [14] consisted of a short-
term assessment of 21 months of data to evaluate simple
relationships between fire activity and climate variables, as well
as fire and vegetation type. The refinement of global fire databases
and accumulation of longer-term records has further enabled such
statistically-based analyses of empirical data, including relation-
ships of global fire activity to anthropogenic explanatory variables
[2] and circum-tropical fires to moisture and energy metrics [8].
However, a thorough multivariate statistical assessment that
captures the complexity of broad global fire-environment
relationships has yet to be undertaken. Furthermore, once
macro-scale fire-environment relationships have been established,
the information provided by statistical parameter estimates can be
used to consider crucial questions about how climate change may
alter the distribution of fire across the globe.
As an alternative to statistical models, simulations using
dynamic global vegetation models (DGVMs) have been used as
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a complementary method to study the global distribution and
effect of fire [15–18]. Although novel ideas about the role of fire in
shaping global vegetation patterns [19] and how fire frequency
might change in the future [15] have been explored with DGVMs,
the simplified approach to simulating fire in many DGVMs has, as
yet, limited their utility in understanding current patterns of fire
around the world. As such, current DGVMs are not capable of
explicitly simulating the extent to which future climate change
may alter fire dynamics [20,21], though refinements to DGVM
fire modules and simulation experiments are promising.
The concept of global pyrogeography — the study of the spatial
distribution of fire across the planet — borrows heavily from
ecology, where three general factors are used to explain the
distribution and abundance of organisms: resource availability,
physiologically appropriate environmental conditions, and dis-
persal ability [22]. In the context of fire, flammable vegetation is
the consumable resource, fire-conducive weather patterns and
their long-term representation (i.e., climate) form the environ-
mental conditions axis, and ignitions are analogous to dispersal
[23]. Admittedly, these dominant constraints on the distribution of
fire are intertwined and complex. Climate is a superordinate
control over both the resources and conditions for fire [7], because
it has a direct, short-term effect on fire weather conditions and an
indirect, longer-term effect in determining the distribution and
quantity of flammable vegetation to burn. In turn, weather and
vegetation conditions affect ignitions, in conjunction with
topographic effects on patterns of lightning strikes [24] and
anthropogenic control over ignition.
Global studies examining how the distribution of fire might
change in the future are necessary to establish the potential impacts
of climate change on vegetation and ecosystems. Local and regional
studies have projected both increases and decreases in future fire
activity, [25–27] however we lack the quantitative estimates needed
to understand what the net effect might be across a warmer planet.
For these reasons, our first goal in this global pyrogeography was to
characterize the observed global fire occurrence pattern (Figure 1)
with an ensemble of multivariate statistical generalized additive
models (GAMs) combining existing fire occurrence, climate, net
primary productivity (NPP), and ignition data. Since many parts of
the globe are fire-free because they have little or no vegetation to
burn, it is informative to distinguish between areas that do not burn
due to limiting consumable resources versus limiting environmental
conditions. To address this issue, we included global vegetation
distribution as an explicit metric for resource availability in one
ensemble of models (FIRENPP), allowing climate to describe
additional variability in fire-conducive conditions. We contrasted
this approach to another ensemble of models (FIREnoNPP) where
climate variables alone were used to describe both resources and
conditions needed for fire. Our results provide a novel multivariate
framework to describe where we currently see wildfire across the
planet. We then apply these models to future climate scenarios,
providing a first estimate of potential changes in the global
distribution of fire. The climate change projections presented here
are based on simulations from the Geophysical Fluid Dynamics
Laboratory Climate Model 2.1 (GFDL CM2.1). Our intent is to
demonstrate the scope of changes that could occur given anticipated
climates under mid-high (A2), and lower (B1) emissions scenarios
proposed by the IPCC Special Report on Emission Scenarios [28].
Materials and Methods
Data
Overview. We constructed statistical GAMs for two
regression model scenarios to characterize current fire patterns,
FIRENPP (explicitly including biomass to burn) and FIREnoNPP
(allowing climate to explain both biomass and environmental
conditions), based on ten random sub-samples of fire [29], climate
[30,31], NPP [32], and ignition [33,34] data. We refer to these
models as each forming a ‘sub-model ensemble’. The ensemble
GAMs were then used with simulated future climate data to
project the potential distribution of fire in the 21st century. We
used global data at a spatial resolution of 100-km (10 000 km2) on
a Behrmann Cylindrical Equal Area projection, resulting in
12 098 pixels over the terrestrial extent of the planet. The
Antarctic continent and small islands were excluded as were
some coastal regions, because of an a priori cutoff rule of at least 1/
3 land fraction in the gridded 2-degree climate data.
Fire. Mapped global vegetation fire locations came from the
European Space Agency’s Advanced and Along Track Scanning
Radiometer (ATSR) World Fire Atlas (algorithm 2) for 1996 to
2006 [29]. The ATSR fire data were registered to our study
domain, where pixels containing at least one fire over the decade
of record were categorized as ‘fire-prone’ and those that did not as
‘fire-free’ (Figure 1A and 1B); alternative classifications will be
explored in future work. Using this criterion, we identified 8399
(69%) pixels as fire-prone over the 10-year period. The ATSR
satellite data include both human- and lightning-caused fires,
which are currently indistinguishable.
There are numerous satellite sensors that can be used to record
wildfire data, and these have been shown to vary somewhat in
their estimates of activity and distribution of fire [35]. We selected
the ATSR data because it provides the longest temporal data set
(10 years) of all fine-resolution global fire products, and post-
processing by Mota et al. [29] provided detailed screening of non-
vegetation fires. Mota et al. [29] used volcanic activity, night-light,
and land cover data as screening tools to remove non-vegetation
fires from this ATSR database alongside statistical techniques that
detected anomalous data clusters. The ATSR senses active night-
time fires at a three day interval to a minimum burning area of
0.01 to 0.1 ha. Night-time acquisition minimizes false positives due
to sun-glint, reflection, and bright soil surfaces, but it potentially
misses short-duration daytime events and summer fires at high
latitudes [10]. For example, Kasischke et al. [10] demonstrated
that many fires may go undetected by the ATSR in the boreal
forest.
The macro-scaled resolution of our study is one way to address
limitations of the fire data, namely omission errors due to detection
difficulties, and a relatively short temporal extent. We tested the
assumption that ATSR fire data were representative of other
global fire products and not biased due to detection difficulties by
comparing the distribution of ATSR fire data to those produced
by the newly available MODIS Collection 5 active fire data [36],
and found the distribution to be very similar (Text S1, Figure S1).
We also tested whether the decade of ATSR fire data were
representative and spatially similar to long-term fire patterns by
comparing ATSR data to a map of large forest fires recorded in
Canada between 1959 to 2002 [37] (Text S2, Figure S2). We
found a strong accord between our macro-scaled ATSR product
and the Canadian fire database, even in the northern boreal
forests where detection of fire can be compromised in finer-scaled
studies [10].
Climate. Our statistical GAMs were built from 17 climate
variables (Table 1) representing potential environmental
conditions controlling fire. These so-called ‘bioclimatic variables’
were calculated climate averages of temperature and precipitation
[30], providing biologically meaningful approximations of recent
historical energy and water balances, as well as environmental
extremes. We used variables calculated for climate data from
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GFDL CM2.1 [31] General Circulation Model (GCM) historical
simulations (1961 to 1990) and from observed climate normals
(1950 to 2000) provided by WorldClim to generate statistical
estimates from GAMs (see Regression modeling, below and Text S3,
Figure S3). The GFDL CM2.1 is a global coupled climate model
developed at NOAA’s Geophysical Fluid Dynamics Laboratory
and was designed to simulate oceanic and atmospheric climate and
variability over a multi-century temporal extent, at a diurnal
resolution [31]. WorldClim is a dataset of interpolated climate
surfaces generated using thin-plate smoothing splines from
weather station data recorded around the world [30]. The
GFDL CM2.1 and WorldClim-based models had very similar
Figure 1. The observed and modeled distribution of fire under current conditions. (A) Cumulative counts of fire activity detected by the
Along Track Scanning Radiometer (ATSR) around the world at a resolution of 100 km over 10 years. (B) The same fire data classified to represent fire-
prone (orange) and fire-free (yellow) parts of the world; note that areas of white within terrestrial boundaries were clipped from the analyses to match
climate data. (C) Mean of normalized relative probability of fire (nPc) for ten FIRENPP sub-models of fire-prone parts of the world under current
conditions. (D) Mean of normalized relative probability of fire (nPc) for ten FIREnoNPP sub-models of fire-prone parts of the world under current
conditions.
doi:10.1371/journal.pone.0005102.g001
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shapes and effect sizes, so given this consistency, we built our
wildfire occurrence models from the GFDL CM2.1 historical
simulation data, from which future fire could then be projected
seamlessly using global 30-year climate averages of variables
simulated for time periods 2010–2039, 2040–2069, and 2070–
2099. The three time periods of simulated future data used in our
study represent future climate conditions corresponding to
increasing concentrations of CO2.
The GFDL CM2.1 simulations for historical and future climate
conditions were generated with a resolution of 2 degrees [31] and
re-gridded to a 100-km resolution across the globe in order to
provide a standardized format compatible with the resolution of
this study (i.e., no statistical downscaling was performed).
Historical model simulations (pre-2000) corresponded to the
Coupled Model Inter-comparison Project ‘‘Twentieth Century
Climate in Coupled Models’’ or 20C3M scenarios [38] which
represent the best efforts to reproduce observed climate over the
past century. Future GFDL CM2.1 simulations (2010–2100) used
here for fire-climate change projections were forced by the IPCC
Special Report on Emission Scenarios (SRES, [28]) mid-high (A2)
emission scenario, in which CO2 concentrations reach 830 ppm
by 2100. A lower emissions scenario, B1, which can be viewed as a
proxy for stabilizing atmospheric CO2 concentrations at or above
550 ppm by 2100, was also examined for comparison. Because of
the known variability in and among GCM outcomes, we also
compared the GFDL CM2.1 future projections of the most
significant climate variables identified in the regression models
with simulations from 15 other atmosphere-ocean general
circulation models (AOGCMs) archived by the IPCC Fourth
Assessment Report Working Group 1 Program for Climate Model
Diagnostics and Intercomparison (PCMDI) database as a simple
assessment of uncertainty in the GFDL-based fire projections.
Vegetation. We quantified the broad-scaled distribution of
flammable vegetation using NPP (Figure S4). Measures of NPP
represent the amount of solar energy converted to plant organic
matter through photosynthesis quantified as elemental units of
carbon per unit time and area, whereas vegetation to burn is
ostensibly the standing stock of biomass represented as units of
carbon per area. The approximately linear relationship between
NPP and biomass [39] invites the use of NPP as a metric of
flammable vegetation, since detailed spatially-gridded, globally
extensive measures of biomass are not readily available [39].
Mapped global NPP was provided by the Carnegie-Ames-
Stanford Approach (CASA) terrestrial carbon model [40] at a
resolution of 0.25 degrees. Estimates of NPP can vary according to
the data and method used; here we used estimates created by
Imhoff and Bounoua [32] using climatology, land cover, solar
radiation, soil texture and vegetation data (AVHRR from 1982–
1998) described therein. We aggregated the raw values to our 100-
km sampling grid using the maximum NPP value recorded from
each pixel. Areas of persistent snow cover (136 pixels) for which no
NPP data were available were given a value of zero.
Ignitions. We examined the potential for human ignition to
limit fire distribution using the Human Footprint (HF) dataset
from the Last of the Wild Project [33] as a proxy for ignition
potential. The HF describes human population pressure, land use
and infrastructure, and access. Lightning, the other major cause of
ignitions, was assessed using the NASA Global Hydrology and
Table 1. Environmental variables used in regression analyses.
Variable Description and Units
Climate Derived from monthly temperature and rainfall values
Annual mean temperature uC
Mean diurnal range mean of monthly (max temp2min temp), uC
Isothermality mean diurnal range/temperature annual range (6100)
Temperature seasonality standard deviation of temperature (6100)
Maximum temperature of warmest month uC
Minimum temperature of coldest month uC
Temperature annual range maximum temperature of warmest month – minimum temperature of coldest month, uC
Mean temperature of wettest month uC
Mean temperature of driest month uC
Mean temperature of warmest month uC
Mean temperature of coldest month uC
Annual precipitation mm/year
Precipitation of wettest month mm/day
Precipitation of driest month mm/day
Precipitation seasonality coefficient of variation
Precipitation of warmest month mm/day
Precipitation of coldest month mm/day
Vegetation
Net primary productivity (NPP) amount of solar energy converted to plant organic matter through photosynthesis (g C per
0.25 decimal degree cell/year).
Ignitions
Lightning flash density flashes/km2/day
Human footprint normalized gradient of human influence (0 to 100)
doi:10.1371/journal.pone.0005102.t001
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Climate Centre Lightning Team’s high resolution annual lightning
climatology which reports annual flash rates per km2 from data
collected between 1995 and 2005 [34]. The results of a
supplemental analysis indicated that there were few areas of the
planet where ignition may be a limiting factor for the 10-year 100-
km resolution of our study (Text S4). Of note, the ignition indices
we used did not distinguish specific lightning characteristics or
human behaviors required for fire ignition. Although ignition
potential was almost never limiting, we still included ignition
agents in the regression models to test whether they reflected any
variation in the likelihood of fire occurring.
Regression modeling
To estimate environmental controls of fire occurrence, we chose
a used-versus-available sampling design analogous to resource
selection functions used in studies of wildlife distribution [41]. This
design allowed us to quantify the particular resources and
conditions conducive to fire by contrasting pixels where fires
occurred against a random sample of pixels using statistical models
that estimate a relative probability of occurrence. We did not
follow a used-versus-unused design because our comparison
between ATSR fire data and the Canadian large fire database
(Text S2, Figure S2) demonstrated that despite the overall
similarity between the databases, fires detected by ATSR during
the 1996–2006 time period do not represent all pixels that are fire-
prone. We used GAMs [42] for statistical modeling in R [43] to
provide flexibility in describing nonlinear relationships between
fire occurrence and environmental variables.
For the FIRENPP (explicitly including biomass to burn) and
FIREnoNPP (allowing climate to explain both biomass and
environmental conditions) scenarios, our sub-model ensemble
approach limited spatial structure in the data, included among-
sample variability, and allowed model cross-validation. Addressing
spatial dependence was particularly important since spatial data
require careful consideration in statistics due to the effect of
autocorrelation on variable [44,45] and model [46] selection. Data
for each sub-model were selected by taking a 15% random sample
(n = 1 260) with replacement from the ‘used’ data (pixels where
fire was detected) and the equivalent number of samples from the
‘available’ data (all pixels). We chose the 15% sample fraction
since variograms of response (fire) and predictor (climate and NPP)
variables indicated the beginnings of a sill in semivariance at a
distance of 15 to 22 pixels (<2 000 km).
We used the GAMs to identify simple and interpretable forms of
candidate variables that described the distribution of fire-prone
parts of the world. Our goal was to develop models that explained
strong patterns of variation in fire distribution while not over-
fitting the observed data. In keeping with this goal, we used the
Akaike Information Criterion (AIC) as a model selection tool
because it is based on the principle of parsimony [47].
Multiple phases were required for model selection and
development. In the FIRENPP models, we first estimated the
relationship between fire occurrence and NPP (Figure S4) to
account for variation in resources to burn, and held this
relationship constant for subsequent model development by using
an offset term. In each of the ten sub-models, the AIC indicated
that the most parsimonious form of the NPP offset term was
estimated with three degrees of freedom, a measure of complexity
in the shape of the relationship. After the inclusion of the NPP
offset, model development proceeded identically for variable
selection in FIRENPP and FIREnoNPP scenarios. Each sub-model
of the ensemble was developed using a forward selection
procedure. Variables were included in an order decided a priori
by rank according to the AIC estimated on independent
relationships between fire occurrence and each environmental
variable. The most parsimonious form of the variable was
subsequently selected using AIC and visual assessment of plots
showing the main effect, standard error estimates, distribution of
the data, and residuals. A reduction in AIC of more than six was
required for the inclusion of the variable in a sub-model.
Explanatory variables strongly correlated to one another were
flagged a priori based on scatter plots and Pearson correlation
coefficients. Although multicollinearity does not affect the use of
the model to infer the mean response under observed conditions, it
can make interpretation of variables difficult because parameter
estimates are conditional on other variables in the model, and
valid predictions can only be made if multicollinearity patterns
hold for the new data [48]. Therefore, as additional terms were
added to each GAM, we checked for changes in the shape and
explanatory power of the existing variables. Variables that entered
the model earliest took priority; lower-ranked variables were
omitted if they were strongly collinear and altered the existing
relationships, even if they otherwise reduced AIC sufficiently.
We assessed the predictive performance of each of the FIRENPP
and FIREnoNPP sub-models using a random sub-sample cross
validation method [49]. Cross validation compares model predic-
tions of training data against a withheld set of data, and the method
proposed by Johnson et al. [49] is the most appropriate for a used-
versus-available sampling design: tests are done on the correlation
between binned estimated values of relative probability from each
model and the frequency of independent withheld values (observed)
in the same bin (here, 30 bins, each with a width of 0.1). The two
most important metrics of those proposed by Johnson et al. [49] were
the tests indicating: i) whether the model is better than random as
indicated by a slope of the regression line between the observed and
estimated values significantly different from zero; and ii) whether the
model fits the data well as indicated by the R2 value of the
relationship between these observed and estimated values.
We ranked the overall importance of explanatory variables from
the ten FIRENPP and FIREnoNPP sub-models by summarizing the
number of times they were selected in the ensemble, as well as the
mean change in AIC when each was removed from a given sub-
model. We also plotted the shape of the dependent response to
each variable to identify and interpret the dominant form of each
relationship.
To illustrate the spatial distribution of fire under current climate
conditions, we calculated a normalized index of relative probabil-
ity scaled between zero and one from parameter estimates of the
sub-model ensembles. Calculations excluded the intercept because
it is not informative in the used-versus-available study design [50].
First, the relative probability for current conditions (rPc) for each
sub-model was calculated as:
rPc~exp b1x1z . . .zbpxp


ð1Þ
where bp are the parameter estimates for each environmental
variable, xp. We then normalized these relative probabilities for
each sub-model and took the mean of the ensemble. The
normalized relative probability for current conditions (nPc) for
each sub-model was calculated as:
nPc~ rPc{min rPcð Þð Þ= max rPcð Þ{min rPcð Þð Þ ð2Þ
Projection of global fire distribution under future climate
conditions
Parameter estimates from the GAMs were applied to future
climate simulations to generate projections of future fire
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distribution. Future climate conditions were estimated for the time
periods 2010–2039, 2040–2069, and 2070–2099 using the SRES
A2 and B1 emissions scenarios. Several methods are available to
generate climate change projections from AOGCM data [51], for
example, by using output directly generated by AOGCMs or by
adding an anomaly or delta value, calculated as the difference
between future and present conditions as simulated by an
AOGCM, to observations. We examined these two approaches
and found that, because of the consistency between fire-climate
relationships estimated for observed and simulated current climate
conditions (Text S3, Figure S3), the first of these approaches would
provide equivalent information to the second, while retaining the
spatial correlation inherent to the physical model that generated
the simulations.
The two GAM ensembles present different ways to think about
the future of fire. The FIRENPP model depicts what the change in
fire distribution might be if the future global pattern of NPP
remained constant; we did not generate climate change projections
from the CASA productivity models, so NPP was essentially held
constant in our FIRENPP model projections. This scenario is
obviously unrealistic over the longer term because of the strong
links between climate and vegetation, but for near-future
projections such as 2010–2039, it may be reasonable to presume
a relatively constant NPP, given that climate induced changes in
fire are expected to occur more quickly than substantial changes in
vegetation via range shifts [52,53]. In contrast, the FIREnoNPP
models predict what the future distribution of fire might be under
the assumption that the climate variables in the regression models
jointly describe vegetation patterns (productivity and structural
form) as well as fire weather conditions. These predictions may
provide an overly liberal view of the near future, because they
essentially remove the dispersal constraints of vegetation change.
However, projections from these FIREnoNPP ensembles could be
more representative of what might be expected later in the
century, such as 2070–2099.
We used a delta index (PD) to assess the differences in current
and future fire distributions. For the PD index, we first calculated
the normalized relative probability of fire for the future (nPf) using
Equation 1, but based on future climate conditions. We then
quantified the changes between future and current relative
probability of fire for each sub-model as:
PD~exp Lf{Lcð Þ ð3Þ
where Lf = ln(rPf) and Lc = ln(rPc) and Lf and Lc are relative
probabilities of the current and future models, respectively. A PD
of less than one indicates a reduction in fire, whereas a value
greater than one indicates an increase. We calculated three delta
indices, PD1039, PD4069, and PD7099, for time periods 2010–2039,
2040–2069, and 2070–2099, restricting the ranges of climate
values for future projections to those of the training models to
avoid spurious prediction. Since analogues existed for virtually all
future climate values, this restriction did not overly constrain
projections. We also removed the terms estimating the relationship
between fire occurrence and lightning flashes from the sub-models
where it was selected, as no information was available to estimate
future lightning patterns from AOGCM simulations.
Lastly, we identified potential ‘‘hotspots of change’’ where fire
was projected to i) invade, by increasing in locations where current
probabilities of fire were low; and ii) retreat, by decreasing in
locations where current probabilities of fire were high. To
highlight the spatial extent and specific locations with the most
potential for near-term shifts, we mapped the distributions of fire
invasion and retreat for scenario A2 at time period 2010–2039
from the FIRENPP ensembles (i.e., PD1039), masking out regions of
the globe with NPP currently less than 96 gC/m2/year (e.g.,
Arctic, Sahara, Greenland). Although this excludes ,21% of
terrestrial lands that now lack biomass to burn, it also
underestimates the amount of future fire invasion into areas
where vegetation could begin to establish in the next few decades.
Selection of the nPc threshold values to isolate areas with relatively
low (for invasion) and high (for retreat) current probabilities of fire
was based on the distribution in values of modeled fire
probabilities around the median value of the current FIRENPP
ensemble.
Results
Statistical modeling of present-day influences on fire
distribution
Statistical modeling using the GAMs indicates that both
resources and conditions contribute to discriminating fire-prone
parts of the world, with similar relationships in both FIRENPP and
FIREnoNPP model ensembles (Table 2, Text S3, Figure S3).
Vegetation NPP had the strongest single relationship of any
predictor variable to the distribution of fire (Table 2, Figure S4),
and eleven additional predictors were selected in both FIRENPP
(after accounting for NPP) and FIREnoNPP ensembles (Table 2).
The maximum number of predictors included in a single sub-
model was seven, and this occurred only once; the mode was five.
Estimated degrees of freedom for the majority of variables ranged
between one and five, generally resulting in simple response
curves. There were limited differences between the predictors
selected in the FIRENPP and FIREnoNPP ensembles (Table 2) and
between the spatial distributions of expected fire probabilities
(Figures 1C and 1D). For example, temperature seasonality was
only selected in FIRENPP models but a closely allied variable,
temperature annual range, was selected in both FIRENPP and
Table 2. The ranked importance of variables selected in
FIRENPP and FIREnoNPP sub-models based on the number of
times the explanatory variable was selected (SEL) and the
mean change in AIC value, which was used to measure the
relative amount of variation explained.
Variable FIRENPP FIREnoNPP
SEL* AIC* SEL AIC
Net primary productivity 10 125 na na
Mean temperature of warmest month 9 16 10 30
Annual precipitation 7 14 10 79
Mean temperature of wettest month 5 13 4 10
Temperature seasonality/temperature annual
range
3/2# 14/25 0/3 na/12
Mean diurnal range 3 10 4 15
Precipitation of driest month 3 7 3 12
Lightning flash density 2 13 5 10
Mean temperature of driest month 2 7 3 12
Precipitation of coldest month 1 13 0 na
Human footprint (HF) 1 10 6 12
*Explanatory variables separated by ‘/’ are highly correlated and were never
selected together in a model, but represented similar environmental trends in
current conditions.
doi:10.1371/journal.pone.0005102.t002
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FIREnoNPP models (Table 2). The human footprint (HF) metric
and lightning flash density explained some variability in fire
occurrence, but only when NPP was not included in the model
(Table 2).
Model cross-validation indicated good discrimination of fire-
prone parts of the world, with tests showing sub-models of both
the FIRENPP and FIREnoNPP ensembles to be significantly better
than random. This diagnostic was demonstrated by slopes of the
correlations between estimated and observed values of relative
probability that were all significantly different from zero, and R2
values between estimated and observed data ranging between
0.96 to 0.98 for the FIRENPP model ensemble and 0.94 to 0.98
for FIREnoNPP ensembles. Visually, the FIRENPP model
ensemble provided finer discrimination of fire-prone parts of
the world (Figures 1C and 1D), especially in regions where
resource levels are high such as the tropics, illustrating areas
where climate variables (FIREnoNPP) were less able than NPP
(FIRENPP) to capture variation in fire occurrence. For example,
the FIREnoNPP ensemble predicted little variation in the
probability of fire across the Amazon and Congo regions of
South America and Africa (Figure 1D) despite containing large
contiguous patches of fire-free areas at the centre of these regions
in the observed data (Figure 1B).
Projection of global fire distribution under future climate
conditions
Given the success of the statistical models in reproducing
present-day fire distributions, we then applied the models to
estimate the change in future fire probabilities (PD) resulting from
the A2 (mid-high emissions) and B1 (lower emissions) climate
projections generated by the GFDL CM2.1 AOGCM. Projected
decreases in fire were indicated by values less than 1.0 and
increases by values greater than 1.0 (Figure 2 and Figures S5, S6).
For the A2 scenario, projected changes in fire over all time periods
ranged from 0.5 to 2.8, depending on the statistical sub-model
used and the geographic location; corresponding results for B1
ranged from 0.7 to 1.9 (Figure 2 and Figures S5, S6). Despite
changes in fire probabilities that deviated progressively more from
current conditions over time and with a higher emissions scenario,
Figure S6 illustrates roughly equivalent increases and decreases in
fire probability over the globe. The coarse spatio-temporal scale
used for this study allowed projections of change without including
finer scaled details known to affect local fire activity such as time
since last fire, since the likelihood of a fire burning through all
biomass available in each 10 000 km2 pixel is relatively unlikely.
It is important to note that the projections shown here are based
on simulations from one AOGCM only. This was a deliberate
Figure 2. Changes in the global distribution of fire-prone pixels under the A2 (mid-high) emissions scenario. An increase from current
conditions (red) is indicated by a PD greater than unity, little or no change (yellow) is indicated by a PD around unit, and a decrease (green) is
indicated by a PD less than unity. Panels show the mean PD for the ensemble of ten FIRENPP (A–C) and FIREnoNPP (D–F) sub-models. Climate projections
include 2010–2039 (A, D), 2040–2069 (B, E) and 2070–2099 (C, F).
doi:10.1371/journal.pone.0005102.g002
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choice, as our primary purpose was to describe the development of
the statistical modeling technique and explore its potential
application to future projections of wildfire. Some measure of
the robustness of these projections can nonetheless be obtained
through comparison of GFDL CM2.1 projections with the average
of those projected by simulations of 15 other AOGCMs archived
in the PCMDI database for the three most significant climate
predictors identified by the statistical analysis: mean temperature
of the warmest month, annual precipitation, and mean temper-
ature of the wettest month (Table 2). This comparison, shown in
Figures S7, S8, S9, suggests that our results may in fact be
indicative of the general magnitude and direction of projected
changes expected from a larger number of AOGCMs. Specifically,
the projections used here appear relatively conservative, close to,
or below the AOGCM ensemble average for the two temperature-
related variables. For precipitation, GFDL CM2.1 projections
tended to lie in the lower half of the distribution, suggesting a slight
tendency towards drier conditions.
Less change in PD values occurred in FIRENPP than FIREnoNPP
sub-model ensembles, largely as a function of including constant
vegetation patterns in the FIRENPP scenario. Both scenarios
showed increasingly higher variability through 2010–2039, 2040–
2069, and 2070–2099 conditions (Figure S6), which translated to
fire distributions that were increasingly dissimilar to those under
current conditions. In terms of geographic location, vast portions
of the continental land area, particularly across North America
and Eurasia, are projected to experience relatively large changes in
fire probabilities (Figure 2 and Figure S5). There were obvious
differences in PD values predicted by FIRENPP and FIREnoNPP
models in northern regions of North America and Eurasia
(Figure 2 and Figure S5), which can be attributed to the absence
of the static NPP variable in the FIREnoNPP model. The remaining
parts of the world had relatively similar changes predicted by the
FIRENPP and FIREnoNPP models.
Areas of projected fire invasion and fire retreat for the near-
term (2010–2039) given A2 emissions using the FIRENPP ensemble
are shown in Figure 3. As described earlier, invasions are defined
by increasing probability of fire in locations with relatively low
current probabilities, and retreat by decreasing probability of fire
in locations with relatively high current probabilities. Current fire
probabilities (Figure 1C) exhibited a median of 0.42, and values
0.08 above and below the median were selected as cutoffs for
current low and high probabilities, respectively. Of the terrestrial
biosphere, 79% of lands met our conservative minimum NPP
criteria, 21% were classified as currently low probability areas
susceptible to fire invasion, and 38% as high probability areas
susceptible to fire retreat. Although other criteria are worth
considering, it appears likely that a substantial fraction of all
terrestrial lands on the planet (one quarter, or 34 M km2 based on
the climate projections used here), may be classified with invasion
(,9% of lands) or retreat (,19% of lands) of fire (Figure 3).
Discussion
Global pyrogeography under current conditions
Wildfire-prone parts of the world span ecological systems
ranging from tropical savannas to boreal forests, characterized by
the interplay of key variables that represent resources and
conditions required for fire activity. As one would expect, we
found that biomass to burn is necessary for wildfires to occur: low
levels of vegetative resources to burn, here represented by low
NPP, resulted in a low probability of fire in areas such as desert
and tundra. In our statistical models, the likelihood of fire
increased with vegetation productivity. This trend has a limit,
however, as other environmental factors become constraints on
fire activity. For example, although some of the most biomass-rich
forests of the planet, such as in peripheral Amazonia and
Indonesia, can be fire-prone, the majority of closed tropical
evergreen forests of the central Amazon and the Congo are
relatively fire-free. These fire-free areas with high NPP rarely
experience environmental conditions that promote biomass
burning — seasonality, episodic wind events, low moisture levels,
or ignitions — given that burnable resources are readily available.
Even during anomalously dry periods, the closed canopy structure
of these biomass-rich rainforests maintains a relatively high
humidity that inhibits burning [54,55]. Our analyses identified
three dominant climate conditions that represent these constraints
at a macro-scale: mean temperature of the warmest month, annual
precipitation, and mean temperature of the wettest month.
The spatial variability in fire occurrence observed across
tropical forests emphasizes the inextricable relationship between
Figure 3. Potential invasion and retreat of fire. The invasion (orange) and retreat (blue) of fire projected by 2010–2039 under the A2 (mid-high)
emissions scenario and based on the FIRENPP ensembles. Invasion was constrained to places with existing vegetation.
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humans and fire, in that fire is dispersed by humans into areas
where resources and conditions would not typically support it
[1,55]. Humans have introduced fire to biomass-rich areas either
by igniting fires within fire-conducive windows of time [56] or by
altering the virtually ‘‘fire-proof’’ vegetation structure [54], often
in association with drought [57]. Although some tropical areas
have been relatively fire-prone for centuries [58,59], many areas of
wet tropical rainforest which only rarely experienced fire in the
past now burn due to accelerating anthropogenic pressure [60].
We found that the overall heterogeneity of fire occurrence in
biomass-rich areas resulted in an asymptotic, yet somewhat
decreasing, probability of burning at the highest NPP levels. The
accompanying variation among our sub-models demonstrates
localized differences in fire activity reflecting very high-NPP areas
as ‘fire frontiers’, or areas undergoing rapid changes in human and
fire activity. Both wet and wet-dry tropical forests are now at the
frontier of anthropogenic development, an ever-advancing zone
that has long been equated with elevated biomass burning due to
land clearing by humans [61].
If the influence of humans essentially means that all areas of the
world supporting sufficient biomass are potentially burnable and
few are ignition-limited, how do we interpret the influence of
environmental conditions on wildfire activity? Clearly, human
activity has been breaking down pyrogeographic barriers [3,60,61]
that regulate lightning-caused fire since the first use of fire by
humans, but our findings indicate that environmental resources
and conditions still play strong roles in determining the global
distribution of vegetation wildfire. Though the spatial patterns of
people, lightning, and biomass are related to some extent,
resources and climate-based variables were stronger constraints
on fire occurrence than ignition-related variables at the coarse
resolution we used. This being said, additional change in the
dynamics of fire management and/or human land use will almost
certainly contribute to altering the future global distribution of fire
alongside climate, presenting a wildcard in future fire-proneness.
Our finding that simple temperature and precipitation gradients
consistently surface as major controls of fire supports an analogy
between the broad distributions of fire-prone areas and Whit-
taker’s [62] seminal categorization of global biomes based on these
two environmental gradients. However, our study also emphasizes
the potential use of synthetic variables to describe the coincident
interactions of energy and water balances. Though some climate
variables considered in our analysis combined elements of
precipitation and temperature such as mean temperature of the
wettest month, none explicitly calculated effective levels of
moisture. For instance, water balance metrics [63,64] have been
shown to provide good discrimination of the occurrence,
abundance, and diversity of some biota at macro-ecological scales
[64]. Dwyer et al. [14] showed that at a global scale the number of
months per year exhibiting a water deficit was strongly associated
with observed fires. The development of global versions of existing
fire-weather/climate metrics such as the Canadian Fire Weather
Index [65], Nesterov Index, or novel metrics such as fire-driven
deforestation potential [8], would also inform syntheses of fire
patterns at broad scales.
Global climate change and fire
Our study demonstrates a new method of examining the future
of global fire activity using AOGCM-based climate projections to
drive statistical models of fire activity. Initial application to
simulations by the GFDL CM2.1 model under mid-high and
lower anthropogenic emissions provides some striking future
outcomes that encourage further development and application of
this framework to more fire metrics and a broader set of AOGCM
simulations. Under the future climate conditions we examined, a
major redistribution of fire-prone areas occurs, with larger changes
observed under scenarios of higher emissions and further into the
future. Yet the net outcome implies that while parts of the world
may experience regional increases in fire activity, others
experience roughly equivalent decreases. Although recent per-
ceived increases in fire through many parts of western North
America are causing ecological, economic, and social concern
[6,66], our results suggest a challenge to any simplistic view that
climate change will lead to more fire in all locations. Rather, we
find that the interplay of changing temperature and precipitation
might result in a rearrangement of global fire probabilities overall,
even as global temperature increases. This does not however,
imply that ecological or social impacts will be minor. Since
projected changes were highly regional and our simulations
suggest the potential for differences that increase across 2010–
2039, 2040–2069, and 2070–2099, fire activity at a given location
may become progressively altered from current conditions be it
through an increase or a decrease in the likelihood of its
occurrence.
Although our study illustrates the magnitude and types of
changes in fire that could be expected in the near future, the
quantitative findings should be interpreted with a suite of caveats.
As already mentioned, additional projections based explicitly on
output from multiple AOGCMs are clearly necessary. Our
statistical models do not incorporate fire-climate-vegetation
feedbacks that could have a further warming effect on global
climate (e.g., through fire-related emissions); in this sense our
projections should be seen as conservative in the amount of
potential change that will occur. In addition, changes in climate
will affect other natural disturbances such as insect outbreaks that
kill or defoliate trees [67], and the result of interactions between
these phenomena and fire activity will be very difficult to predict.
Fire occurrence is only one parameter of a fire regime, and
additional studies are necessary to examine potential changes in
other components such as area burned, fire intensity or
seasonality. Furthermore, the evolution of fire management
through suppression techniques, public awareness, and policy
changes is also likely to change fire activity in the future.
Projections of fire occurrence were carried out using a pair of
modeling ensembles, with one scenario holding biomass structure
constant at current levels (FIRENPP), and one scenario where
vegetation essentially tracks climate changes (FIREnoNPP). In the
latter, larger changes were observed in fire probabilities, generated
by the compensatory, larger overall effect sizes estimated for
climate variables in the FIREnoNPP models, including annual
precipitation and mean temperature of the warmest month.
Emergent differences between projections from FIRENPP and
FIREnoNPP ensembles were most apparent in the far north of
North America and Eurasia suggesting that some environmental
conditions conducive to combustion and fire spread are likely to
increase there over the next decades, yet the limited availability of
biomass to burn, as demonstrated by models where NPP was held
constant, could buffer dramatic near-future increases in fire
activity. The remainder of the world showed similar changes in the
future distribution of fire for both model ensembles. We contended
earlier that since the FIREnoNPP ensembles for 2070–2099
represented a scenario that notionally included a shift in biomass
patterns, it was more appropriate for longer-term projections due
to the inevitable but slow range shifts in vegetation expected with
climate change [52].
We classified areas projected to transition from low to high
probabilities of fire in the near future (2010–2039) as at risk of fire
invasion and areas projected to transition from high to low as at
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risk of fire retreat. An ecosystem that has experienced little or no
fire which then incurs a higher probability or frequency of fire,
such as a desert or rainforest, may be fire-sensitive and particularly
susceptible to changes in community structure or ecosystem state
due to increases in fire activity [60,68,69]. At the other end of the
spectrum, decreases in fire may also affect species or communities
that have adaptations that enable them to thrive in fire-prone
ecosystems and may depend on narrow ranges of fire intervals for
persistence. In nature, species are not simply adapted to fire, but to
a given set of parameters that represent a fire regime. Our fire
invasion and retreat metrics thus identify potential ‘hotspots of
change’ where altered fire-proneness may catalyze relatively rapid
changes in ecosystem structure, acting alongside the more gradual
effect of climate on individual species tolerances. In these hotspots
of change, it would be particularly valuable to quantify changes in
additional fire regime parameters, for example fire intensity, a
measure that includes not only changes in the conditions for fire,
but also the resources available to burn under those conditions.
The rate at which fire activity may change in the future relative
to rates of climate-induced changes in vegetation ranges is highly
uncertain. Rapid vegetation changes are possible, such as when
non-native grasses rapidly invade desert systems under suitable
environmental conditions [70,71]. However, the potential for
relatively rapid and large changes in fire probabilities seen in our
FIRENPP ensembles for 2010–2039 illustrate that in the near term,
fire activity could change faster than many terrestrial species may
be able to accommodate. For models projecting the future of
species distributions, especially that of plants [72], such rapid
change underlines the importance of developing methods to
explicitly integrate how fire activity affects vegetation, in addition
to species range changes based on plant-climate relationships
alone.
Our models provide global, quantitative projections of wildfire
that can be compared to existing studies of climate change to
gauge not only their agreement in scope and location, but also
disparities that can direct refinements in subsequent studies. For
example, using a relatively simple parameterization of fire in a
DGVM driven by input from multiple AOGCMs, Scholze et al.
[15] describe global changes in wildfire frequency that align with
our estimates in many areas, also generally supporting the concept
of a net global balance between increases and decreases in future
fire. In fact, such consilience between two very different modeling
frameworks raises the possibility of new hypotheses about
energetically-regulated limits that amount to a global ‘‘carrying
capacity’’ for fire. We are unaware of other global studies
estimating climate-induced changes in wildfire, so our statistical
framework provides a new, much-needed and complementary
approach to predicting future global pyrogeography.
Studies of climate-induced changes in fire have been imple-
mented at regional scales, and these can also be used to interpret
our results. For example, a DGVM and output from a GCM was
used to simulate future fire regimes in Alaska, predicting a relative
decrease in future area burned in central Alaska and increases in
future area burned along the southern and western coasts [73],
which match projections from our models. Using a regression
approach similar to ours, anticipated changes in fire return
interval were shown across boreal regions of North America by the
end this century [26] that correspond most closely with our models
where biomass was unconstrained. High-resolution regional
climate simulations were used to suggest increased future fire risk
across northern and eastern Australia [27], which aligns with
outcomes from our models with biomass constrained, though our
projections suggest more of a long-term decrease in fire in our
unconstrained models. Finally, projected changes in fire weather
indices for North America and Europe using simulated data from
the Canadian GCM [74] are in accord with our projections in
central and eastern France, but not in Fennoscandia.
Conclusion
In this study, we first developed a statistical modeling
framework capable of reproducing current-day global fire patterns
and describing the influence of underlying environmental controls
on those patterns. We then examined the global scope of, and
potential regions likely to be affected by, severely altered
probabilities of fire using statistical models and an illustrative set
of climate projections. Our global pyrogeography provides a new,
multivariate quantification of the current distribution of vegetation
fires across the planet that is both coherent with our knowledge of
global fire patterns and capable of projecting potential changes in
wildfire for the future.
The original impetus for this work was to complement the
subjective, expert-driven assessment of global fire regimes devised
in the Global Fire Assessment, spearheaded by The Nature
Conservancy [75]. Our hope is that this approach to global
pyrogeography will continue to develop as a framework for
providing robust estimates of potential perturbations in global fire
patterns and future ecosystem changes, which could then
complement and inform global DGVM simulations. Our proposed
framework would also benefit from the inclusion of more
advanced assessment of fire-human dynamics, the use of additional
fire metrics (e.g., area burned, intensity, seasonality), updates in
global fire products (e.g., MODIS), and the quantification of
AOGCM-related uncertainty [76], as this information becomes
available. Given the dearth of information on global fire in the
context of climate change [77], the utility and importance of
coarse spatiotemporal studies can only increase, providing
informative and synthetic insights about global wildfire and the
extent of changes that could be expected in the future.
Supporting Information
Figure S1 The distribution of fire detected by MODIS. Data are
displayed as the occurrence of fire at a spatial resolution of
100 km, between November 2000 and December 2006. Note that
areas of white within terrestrial boundaries were clipped to match
the fire-climate analyses.
Found at: doi:10.1371/journal.pone.0005102.s001 (0.40 MB TIF)
Figure S2 Spatial comparison between a decade of ATSR fire
data and fires recorded in the Canadian Large Fire Database
(LFDB). Grey represents areas where no fire was detected, red
shows areas where fire was detected in both the ATSR and LFDB,
orange shows areas where fires were only detected by ATSR, and
yellow shows areas where fires were only documented in the
LFDB.
Found at: doi:10.1371/journal.pone.0005102.s002 (0.09 MB TIF)
Figure S3 The modeled response, f(x), for the five most highly
ranked climate variables of the FIRENPP ensemble. Response
curves were estimated from fire occurrence and simulated GFDL
CM2.1 data (A), and observed WorldClim data (B). Grey lines are
estimates from each of the sub-models in the ensemble and black
lines are the mean of these estimates. Descriptions of climate
variables are found in Table 1 of the main text. Note that plotting
axes vary among the variables; the x-axis for ‘‘Annual precipita-
tion’’ is presented on a log10 scale.
Found at: doi:10.1371/journal.pone.0005102.s003 (0.13 MB TIF)
Figure S4 The global distribution of NPP, and the relationship
between fire occurrence and NPP estimated with the ten FIRENPP
Global Pyrogeography
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sub-models. Values on x-axis are presented as approximate g C/
m2/year, by dividing data (g C/0.25 decimal degree cell) by
7.76108. The values for NPP are clipped to the extent of the
GFDL CM2.1 climate data used in the regression models, such
that areas of white along some coast-lines indicate areas not
included in the study.
Found at: doi:10.1371/journal.pone.0005102.s004 (1.09 MB TIF)
Figure S5 Changes in the global distribution of fire-prone pixels
under the B1 (low) emissions scenario. An increase from current
conditions (red) is indicated by PD greater than unity, little or no
change (yellow) is indicated by PD around unity, and a decrease
(green) is indicated by PD less than unity. Panels show the mean PD
for the ensemble of ten FIRENPP (A–C) and FIREnoNPP (D–F) sub-
models. Climate projections include 2010–2039 (A, D), 2040–
2069 (B, E) and 2070–2099 (C, F).
Found at: doi:10.1371/journal.pone.0005102.s005 (1.38 MB TIF)
Figure S6 Distribution in values of change in the relative
probability of fire (PD) under future conditions.
Found at: doi:10.1371/journal.pone.0005102.s006 (0.55 MB TIF)
Figure S7 A comparison of mean temperature of the warmest
month from 15 AOGCMs. Periods of comparison include: 2010–
2039 (A), 2040–2069 (B) and 2070–2099 (C), under the SRES A2
(mid-high) emissions scenario. The GFDL CM2.1 projections
(outlined) fall in the mid-range of all models - half of the models
show warmer temperatures and half show cooler.
Found at: doi:10.1371/journal.pone.0005102.s007 (3.39 MB TIF)
Figure S8 Comparison of annual precipitation from 15
AOGCMs. Periods of comparison include: 2010–2039 (A),
2040–2069 (B) and 2070–2099 (C), under the SRES A2 (mid-
high) emissions scenario. The GFDL CM2.1 projections (outlined)
are in the lower half of the 15 models. Although there are several
models that project significantly drier conditions than GFDL
CM2.1, in general by end-of-century its projections show smaller
precipitation increases (across northern Europe, along the west
coasts of the Americas, and in mid-Africa) than the majority of
models.
Found at: doi:10.1371/journal.pone.0005102.s008 (3.87 MB TIF)
Figure S9 Comparison of mean temperature of the wettest
month from 15 AOGCMs. Periods of comparison include: 2010–
2039 (A), 2040–2069 (B) and 2070–2099 (C) under the SRES A2
(mid-high) emissions scenario. The GFDL CM2.1 projections
(outlined) are relatively conservative - by the end of the century,
projections are in the lower third of the 15 models.
Found at: doi:10.1371/journal.pone.0005102.s009 (3.46 MB TIF)
Text S1 A comparison between ATSR fire data from 1996 to
2006 and MODIS Collection 5 active fire data from 2000 to 2006.
Found at: doi:10.1371/journal.pone.0005102.s010 (0.02 MB
DOC)
Text S2 A comparison between a decade of ATSR fire data and
fires recorded in the Canadian Large Fire Database.
Found at: doi:10.1371/journal.pone.0005102.s011 (0.02 MB
DOC)
Text S3 Relationships estimated between historical fire occur-
rence and climate variables using observed (WorldClim) and
simulated (GFDL CM2.1) data.
Found at: doi:10.1371/journal.pone.0005102.s012 (0.02 MB
DOC)
Text S4 Assessment to determine if ignition might limit patterns
of fire occurrence, based on the Human Footprint and lightning
data.
Found at: doi:10.1371/journal.pone.0005102.s013 (0.02 MB
DOC)
Acknowledgments
Thank you to The Nature Conservancy’s Global Fire Initiative and
NSERC for providing funding for M.A.K. to pursue this research. Jose´
Pereira kindly provided the screened ATSR World Fire Atlas data. Duarte
Oom and Eric Waller provided valuable conversations during the
preparation of the manuscript.
Author Contributions
Conceived and designed the experiments: MAK MAM MAP. Analyzed
the data: MAK JVD KH. Contributed reagents/materials/analysis tools:
MAK MAM JVD KH. Wrote the paper: MAK MAM MAP KH.
References
1. Lavorel S, Flannigan MD, Lambin EF, Scholes MC (2006) Vulnerability of land
systems to fire: Interactions among humans, climate, the atmosphere, and
ecosystems. Mitigation Strategies for Global Change; DOI 10.1007/s11027-
006-9046-5.
2. Chuvieco E, Giglio L, Justice C (2008) Global characterization of fire activity:
toward defining fire regimes from Earth observation data. Global Change
Biology 14: 1–15.
3. Calef MP, McGuire AD, Chapin FS (2008) Human influences on wildfire in
Alaska from 1988 through 2005: An analysis of the spatial patterns of human
impacts. Earth Interactions 12.
4. Malilay J (1999) A review of factors affecting the human health impacts
of air pollutants from forest fires; Lima, Peru, 1988. World Health
Organization.
5. van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Kasibhatla PS, et al.
(2006) Interannual variability in global biomass burning emissions from 1997 to
2004. Atmospheric Chemistry and Physics 6: 3423–3441.
6. Moritz MA, Stephens SL (2008) Fire and sustainability: considerations for
California’s altered future climate. Climatic Change 87: S265–S271.
7. Meyn A, White PS, Buhk C, Jentsch A (2007) Environmental drivers of large,
infrequent wildfires: the emerging conceptual model. Progress in Physical
Geography 31: 287–312.
8. van der Werf GR, Randerson JT, Giglio L, Gobron N, Dolman AJ (2008)
Climate controls on the variability of fires in the tropics and subtropics. Global
Biogeochemical Cycles 22.
9. Giglio L, van der Werf GR, Randerson JT, Collatz GJ, Kasibhatla P (2006)
Global estimation of burned area using MODIS active fire observations.
Atmospheric Chemistry and Physics 6: 957–974.
10. Kasischke ES, Hewson JH, Stocks B, van der Werf G, Randerson J (2003) The
use of ATSR active fire counts for estimating relative patterns of biomass
burning - a study from the boreal forest region. Geophysical Research Letters
30.
11. Riano D, Ruiz JAM, Isidoro D, Ustin SL (2007) Global spatial patterns and
temporal trends of burned area between 1981 and 2000 using NOAA-NASA
Pathfinder. Global Change Biology 13: 40–50.
12. Le Page Y, Pereira JMC, Trigo R, da Camara C, Oom D, et al. (2007) Global
fire activity patterns (1996–2006) and climatic influence: an analysis using the
World Fire Atlas. Atmospheric Chemistry and Physics Discussions 7:
17299–17338.
13. Carmona-Moreno C, Belward A, Malingreau JP, Hartley A, Garcia-Alegre M,
et al. (2005) Characterizing interannual variations in global fire calendar using
data from Earth observing satellites. Global Change Biology 11: 1537–1555.
14. Dwyer E, Gregoire JM, Pereira JMC (2000) Climate and vegetation as driving
factors in global fire activity. In: Beniston M, ed. Biomass burning and its inter-
relationship with the climate system. London: Kluwer Academic Publishers. pp
358.
15. Scholze M, Knorr W, Arnell NW, Prentice IC (2006) A climate-change risk
analysis for world ecosystems. Proceedings of the National Academy of Sciences
of the United States of America 103: 13116–13120.
16. Thonicke K, Venevsky S, Sitch S, Cramer W (2001) The role of fire disturbance
for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation
Model. Global Ecology and Biogeography 10: 661–677.
17. Venevsky S, Maksyutov S (2007) SEVER: A modification of the LPJ global
dynamic vegetation model for daily time step and parallel computation.
Environmental Modelling & Software 22: 104–109.
18. Sitch S, Huntingford C, Gedney N, Levy PE, Lomas M, et al. (2008) Evaluation
of the terrestrial carbon cycle, future plant geography and climate-carbon cycle
feedbacks using five Dynamic Global Vegetation Models (DGVMs). Global
Change Biology 14: 2015–2039.
Global Pyrogeography
PLoS ONE | www.plosone.org 11 April 2009 | Volume 4 | Issue 4 | e5102

19. Bond WJ, Woodward FI, Midgley GF (2005) The global distribution of
ecosystems in a world without fire. New Phytologist 165: 525–537.
20. Arora VK, Boer GJ (2005) Fire as an interactive component of dynamic
vegetation models. Journal of Geophysical Research-Biogeosciences 110.
21. Purves D, Pacala S (2008) Predictive models of forest dynamics. Science 320:
1452–1453.
22. Soberon J (2007) Grinnellian and Eltonian niches and geographic distributions
of species. Ecology Letters 10: 1115–1123.
23. Parisien MA, Moritz MA (2009) Environmental controls on the distribution of
wildfire at multiple spatial scales. Ecological Monographs 79: 127–154.
24. Dissing D, Verbyla DL (2003) Spatial patterns of lightning strikes in interior
Alaska and their relations to elevation and vegetation. Canadian Journal of
Forest Research-Revue Canadienne De Recherche Forestiere 33: 770–782.
25. Krawchuk MA, Cumming SG, Flannigan MD (2008) Predicted changes in fire
weather suggest increases in lightning fire initiation and future area burned in
the mixedwood boreal forest. Climatic Change in press.
26. Balshi MS, Mcguire AD, Duffy P, Flannigan MD, Walsh J, et al. (2008)
Assessing the response of area burned to changing climate in western boreal
North America using a Multivariate Adaptive Regression Splines (MARS)
approach. Global Change Biology 14: 1–23.
27. Pitman AJ, Narisma GT, McAneney J (2007) The impact of climate change on
the risk of forest and grassland fires in Australia. Climatic Change 84: 383–401.
28. Nakicenovic N, et al. (2000) IPCC special report on emissions scenarios.
Cambridge, U.K.: Cambridge University Press.
29. Mota BW, Pereira JMC, Oom D, Vasconcelos MJP, Schultz M (2006) Screening
the ESA ATSR-2 World Fire Atlas (1997–2002). Atmospheric Chemistry and
Physics 6: 1409–1424.
30. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high
resolution interpolated climate surfaces for global land areas. International
Journal of Climatology 25: 1965–1978.
31. Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, et al. (2006) GFDL’s
CM2 global coupled climate models. Part I: Formulation and simulation
characteristics. Journal of Climate 19: 643–674.
32. Imhoff ML, Bounoua L (2006) Exploring global patterns of net primary
production carbon supply and demand using satellite observations and statistical
data. Journal of Geophysical Research-Atmospheres 111.
33. Sanderson EW, Jaiteh M, Levy MA, Redford KH, Wannebo AV, et al. (2002)
The human footprint and the last of the wild. Bioscience 52: 891–904.
34. Christian HJ, Blakeslee RJ, Boccippio DJ, Boeck WL, Buechler DE, et al. (2003)
Global frequency and distribution of lightning as observed from space by the
Optical Transient Detector. Journal of Geophysical Research-Atmospheres 108.
35. Boschetti L, Eva HD, Brivio PA, Gregoire JM (2004) Lessons to be learned from
the comparison of three satellite-derived biomass burning products. Geophysical
Research Letters 31.
36. Giglio L, Csiszar I, Justice CO (2006) Global distribution and seasonality of
active fires as observed with the Terra and Aqua Moderate Resolution Imaging
Spectroradiometer (MODIS) sensors. Journal of Geophysical Research-Bio-
geosciences 111.
37. Stocks BJ, Mason JA, Todd JB, Bosch EM, Wotton BM, et al. (2002) Large
forest fires in Canada, 1959–1997. Journal of Geophysical Research-Atmo-
spheres 108.
38. Covey C, AchutaRao KM, Cubasch U, Jones P, Lambert SJ, et al. (2003) An
overview of results from the Coupled Model Intercomparison Project. Global
and Planetary Change 37: 103–133.
39. Kindermann GE, McAllum I, Fritz S, Obersteiner M (2008) A global forest
growing stock, biomass and carbon map based on FAO statistics. Silva Fennica
42: 387–396.
40. Potter C, Klooster S, Myneni R, Genovese V, Tan PN, et al. (2003) Continental-
scale comparisons of terrestrial carbon sinks estimated from satellite data and
ecosystem modeling 1982–1998. Global and Planetary Change 39: 201–213.
41. Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP (2002)
Resource selection by animals. London: Kluwer Academic Publishers. 221 p.
42. Hastie TJ, Tibshirani RJ (1990) Generalized additive models. London:
Chapman and Hall.
43. R (2008) R: A Language and Environment for Statistical Computing. Vienna,
Austria: R Foundation for Statistical Computing.
44. Legendre P, Dale MRT, Fortin MJ, Gurevitch J, Hohn M, et al. (2002) The
consequences of spatial structure for the design and analysis of ecological field
surveys. Ecography 25: 601–615.
45. Currie DJ (2007) Disentangling the roles of environment and space in ecology.
Journal of Biogeography 34: 2009–2011.
46. Hoeting JA, Davis RA, Merton AA, Thompson SE (2006) Model selection for
geostatistical models. Ecological Applications 16: 87–98.
47. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a
practical information-theoretic approach. New York: Springer. 488 p.
48. Neter J, Wasserman W, Kutner MH (1989) Applied linear regression models.
Boston: Irwin. 667 p.
49. Johnson CJ, Nielsen SE, Merrill EH, McDonald TL, Boyce MS (2006) Resource
selection functions based on use-availability data: Theoretical motivation and
evaluation methods. Journal of Wildlife Management 70: 347–357.
50. Hosmer DW, Lemeshow S (2000) Applied Logistic Regression. New York: Wiley
and Sons, Inc. 375 p.
51. IPCC-TGICA (2007) General guidelines on the use of scenario data for climate
impact and adaptation assessment. Version 2. 66 p.
52. Clark JS, Fastie C, Hurtt G, Jackson ST, Johnson C, et al. (1998) Reid’s paradox
of rapid plant migration - Dispersal theory and interpretation of paleoecological
records. Bioscience 48: 13–24.
53. Neilson RP, Pitelka LF, Solomon AM, Nathan R, Midgley GF, et al. (2005)
Forecasting regional to global plant migration in response to climate change.
Bioscience 55: 749–759.
54. Cochrane MA, Laurance WF (2002) Fire as a large-scale edge effect in
Amazonian forests. Journal of Tropical Ecology 18: 311–325.
55. Goldammer JG, Price C (1998) Potential impacts of climate change on fire
regimes in the tropics based on MAGICC and a GISS GCM-derived lightning
model. Climatic Change 39: 273–296.
56. Laris P, Wardell D (2006) Good, bad or ‘necessary evil’? Reinterpreting the
colonial burning experiments in the savanna landscapes of West Africa. The
Geographical Journal 172: 271–290.
57. Aragao L, Malhi Y, Roman-Cuesta RM, Saatchi S, Anderson LO, et al. (2007)
Spatial patterns and fire response of recent Amazonian droughts. Geophysical
Research Letters 34.
58. Clement RM, Horn SP (2001) Pre-Columbian land-use history in Costa Rica: a
3000-year record of forest clearance, agriculture and fires from Laguna Zoncho.
Holocene 11: 419–426.
59. League BL, Horn SP (2000) A 10 000 year record of Paramo fires in Costa Rica.
Journal of Tropical Ecology 16: 747–752.
60. Cochrane MA (2003) Fire science for rainforests. Nature 421: 913–919.
61. Marlon JR, Bartlein PJ, Carcaillet C, Gavin DG, Harrison SP, et al. (2008)
Climate and human influences on global biomass burning over the past two
millennia. Nature Geosci advanced online publication.
62. Whittaker RH (1975) Communities and ecosystems. New York: Macmillan.
63. Thornthwaite CW (1940) Atmospheric moisture in relation to ecological
problems. Ecology 21: 17–28.
64. Stephenson NL (1990) Climatic Control of Vegetation Distribution - the Role of
the Water-Balance. American Naturalist 135: 649–670.
65. van Wagner C (1987) Development and structure of the Canadian Forest Fire
Weather Index System. Ottawa, Ontario, Canada.
66. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and
earlier spring increase western US forest wildfire activity. Science 313: 940–943.
67. Hicke JA, Logan JA, Powell J, Ojima DS (2006) Changing temperatures
influence suitability for modeled mountain pine beetle (Dendroctonus ponder-
osae) outbreaks in the western United States. Journal of Geophysical Research-
Biogeosciences 111.
68. Holling CS (1973) Resilience and stability of ecological systems. Annual Review
of Ecology and Systematics 4: 1–23.
69. Scheffer M, Carpenter S, Foley JA, Folke C, Walker B (2001) Catastrophic shifts
in ecosystems. Nature 413: 591–596.
70. Brooks ML, Matchett JR (2006) Spatial and temporal patterns of wildfires in the
Mojave Desert, 1980–2004. Journal of Arid Environments 67: 148–164.
71. D’Antonio CM, Vitousek PM (1992) Biological Invasions by Exotic Grasses, the
Grass Fire Cycle, and Global Change. Annual Review of Ecology and
Systematics 23: 63–87.
72. Loarie SR, Carter BE, Hayhoe K, McMahon S, Moe R, et al. (2008) Climate
Change and the Future of California’s Endemic Flora. PLoS ONE 3: e2502.
doi:10.1371/journal.pone.0002502.
73. Bachelet D, Lenihan J, Neilson R, Drapek R, Kittel T (2005) Simulating the
response of natural ecosystems and their fire regimes to climatic variability in
Alaska. Canadian Journal of Forest Research-Revue Canadienne De Recherche
Forestiere 35: 2244–2257.
74. Flannigan MD, Bergeron Y, Engelmark O, Wotton BM (1998) Future wildfire in
circumboreal forests in relation to global warming. Journal of Vegetation
Science 9: 469–476.
75. Shlisky A, Waugh J, Gonzalez P, Gonzalez M, Manta M, et al. (2007) Fire,
ecosystems and people: Threats and strategies for global biodiversity
conservation. Arlington: The Nature Conservancy.
76. Beaumont L, Hughes L, Pitman AJ (2008) Why is the choice of future climate
scenarios for species distribution modelling important? Ecology Letters 11:
1135–1146.
77. Fischlin A, Midgley GF, Price J, Leemans R, Gopal B, et al. (2007) Ecosystems,
their properties, goods, and services. In: Parry ML, Canziani OF, Palutikof
JP, van der Linden PJ, Hanson CE, eds. Climate Change 2007: Impacts,
Adaptation and Vulnerability Contribution of Working Group II to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change.
Cambridge: Cambridge University Press. pp 211–272.
Global Pyrogeography
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