Ecological Monographs, 79(2), 2009, pp. 201–219
 2009 by the Ecological Society of America
Vegetation mediated the impacts of postglacial climate change on fire
regimes in the south-central Brooks Range, Alaska
PHILIP E. HIGUERA,1,5 LINDA B. BRUBAKER,1 PATRICIA M. ANDERSON,2 FENG SHENG HU,3 AND THOMAS A. BROWN4
1College of Forest Resources, University of Washington, Seattle, Washington 98195 USA
2Department of Earth and Space Sciences and Quaternary Research Center, University of Washington,
Seattle, Washington 98195 USA
3Department of Plant Biology, University of Illinois, Urbana, Illinois 61801 USA
4CAMS, Lawrence Livermore National Laboratory, Livermore, California 94551 USA
Abstract. We examined direct and indirect impacts of millennial-scale climate change on
fire regimes in the south-central Brooks Range, Alaska, USA, using four lake sediment records
and existing paleoclimate interpretations. New techniques were introduced to identify charcoal
peaks semi-objectively and to detect statistical differences between fire regimes. Peaks in
charcoal accumulation rates provided estimates of fire return intervals (FRIs), which were
compared among vegetation zones identified by fossil pollen and stomata. Climatic warming
between ca. 15 000–9000 yr BP (calendar years before Common Era [CE] 1950) coincided with
shifts in vegetation from herb tundra to shrub tundra to deciduous woodlands, all novel
species assemblages relative to modern vegetation. Two sites cover this period and show
decreased FRIs with the transition from herb to Betula-dominated shrub tundra ca. 13 300–
14 300 yr BP (FRImean ¼ 144 yr; 95% CI ¼ 120–169 yr), when climate warmed but remained
cooler than present. Although warming would have favored shorter FRIs in the shrub tundra,
the shift to more continuous, flammable fuels relative to herb tundra was probably a more
important cause of increased burning. Similarly, a vegetation shift to Populus-dominated
deciduous woodlands overrode the influence of warmer- and drier-than-present summers,
resulting in lower fire activity from ca. 10 300–8250 yr BP (FRImean ¼ 251 yr; 95% CI¼ 156–
347 yr). Three sites record the mid-to-late Holocene, when climatic cooling and moistening
allowed Picea glauca forest–tundra and P. mariana boreal forests to establish ca. 8000 and
5500 yr BP, respectively. FRIs in forest–tundra were either similar to or shorter than those in
the deciduous woodlands (FRImean range¼ 131–238 yr). The addition of P. mariana ca. 5500
yr BP increased landscape flammability, overrode the effects of climatic cooling and
moistening and resulted in lower FRIs (FRImean¼145 yr; 95% CI¼130–163). Overall, shifts in
fire regimes were strongly linked to changes in vegetation, which were responding to
millennial-scale climate change. We conclude that shifts in vegetation can amplify or override
the direct influence of climate change on fire regimes, when vegetation shifts significantly
modify landscape flammability. Our findings emphasize the importance of biophysical
feedbacks between climate, fire, and vegetation in determining the response of ecosystems to
past, and by inference, future climate change.
Key words: Alaska (USA); arctic; boreal forest; charcoal analysis; climate change; deciduous
woodland; fire history; landscape flammability; pollen analysis; shrub tundra; tundra.
INTRODUCTION
Recent warming in northern high latitudes (Overpeck
et al. 1997, Serreze et al. 2000, ACIA 2004) has initiated
a variety of changes in vegetation and fire regimes,
including population expansion and increased growth of
trees and shrubs (Lloyd 2005, Tape et al. 2006) and
increased area burned across boreal forests (Kasischke
and Turetsky 2006, Soja et al. 2007). Although the
response of fire regimes to climate change is complex
and will vary regionally (Flannigan et al. 1998, Bergeron
et al. 2004), there is general agreement that area burned
across arctic and boreal regions will increase over the
next century as climate change lengthens the fire season,
decreases effective moisture, and increases ignition rates
(Stocks et al. 1998, ACIA 2004, Calef et al. 2005,
Flannigan et al. 2005, Girardin and Mudelsee 2008).
These predictions are based primarily on short-term
fire–climate relationships established in recent decades
(Johnson 1992, Kasischke et al. 2002, Duffy et al. 2005,
Girardin and Sauchyn 2008), but paleoecological studies
also suggest that changes in relative moisture have
influenced fire regimes throughout the Holocene (Car-
caillet and Richard 2000, Millspaugh et al. 2000,
Manuscript received 10 December 2007; revised 13 June
2008; accepted 20 June 2008. Corresponding Editor: S. T.
Jackson.
5 Present address: Department of Earth Sciences, 200
Traphagen Hall, Montana State University, Bozeman,
Montana 59717 USA. E-mail: philip.higuera@montana.edu
201

Carcaillet et al. 2001, Lynch et al. 2002, 2004b).
Nevertheless, the response of fire regimes to climate
change will also depend strongly upon feedbacks
between climate, fire, and vegetation (Rupp et al. 2000,
ACIA 2004, Chapin et al. 2004, McGuire et al. 2006),
complicating predictions of fire based on direct climate–
fire relationships.
Vegetation can alter the direct link between climate
and fire by influencing the abundance, structure, and
moisture content of fuels across space and time. In
ecosystems with low and/or discontinuous fuels, fire is
limited primarily by the lack of burnable materials, even
though weather and climate may be favorable for
burning (e.g., the Mojave Desert in the southwestern
U.S.; Brooks and Matchett 2006). In ecosystems
supporting dense, continuous vegetation, fires are
limited primarily by weather and climate conditions
that promote fuel drying and ignitions over daily to
weekly time scales (e.g., North American boreal forest;
Johnson 1992, Bessie and Johnson 1995). However, even
in these ecosystems, vegetation can have a subtle,
secondary influence on fires regimes. For example, in
areas of Alaska where black spruce (Picea mariana Mill.
BSP.) boreal forests provide abundant flammable fuels
(south of the Brooks Range and north of the Alaska
Range; Fig. 1), area burned was positively correlated
with tree density (Kasischke et al. 2002). In these forests,
the probability of fire also increases with stand age
because fuel accumulation at decadal time scales
increases the probability of fire occurrence and spread
(Yarie 1981, Schimmel and Granstrom 1997, Lynch et
al. 2002). Given the complex interactions between fuels
and climate, vegetation change can have profound
impacts on fire regimes, resulting in fire regimes shifts
that are opposite or independent of climate’s direct
influence on fire (Rupp et al. 2002, Hu et al. 2006, Tinner
et al. 2008).
We used a paleoecological approach to examine the
relationships between climate, vegetation, and fire
regimes in the foothills of the south-central Brooks
Range, Alaska (Fig. 1), where millennial-scale climate
and vegetation histories have been investigated for
several decades (Anderson et al. 2004). Specifically,
our goal was to examine alternative hypothesis of
climatic vs. vegetational controls over fire regimes by
documenting fire history and vegetation assemblages
that covered the study region from late glaciation and
through the Holocene (ca. 15 000 yr BP to present). We
used macroscopic charcoal from lake sediments to
reconstruct fire occurrence and statistically compare fire
return intervals (FRIs, years between consecutive fires)
among vegetation zones inferred from fossil pollen,
stomata stratigraphy, and modern analog analysis. If
climatic variations were the dominant control of fire
regimes, changes in fire occurrence between vegetation
zones should be consistent with direct climate–fire
relationships and relatively independent of vegetation
characteristics (e.g., Carcaillet et al. 2001). However, if
vegetation was the dominant control of fire regimes,
changes in fire occurrence between vegetation zones
should be consistent with the role that fuel type plays in
determining landscape flammability (e.g., Lynch et al.
2002) and possibly unexpected given the direct effects of
climate change on fire regimes. In addition, our study
provides examples of how direct and indirect impacts of
climate change may shape future fire regimes in arctic
and boreal ecosystems.
STUDY LAKES AND REGIONAL SETTING
We examined sediment cores from four lakes along a
120-km east–west transect in the foothills of the south-
central Brooks Range, Alaska, USA (Table 1, Fig. 1).
Modern climate in the study region is continental.
January and July mean maximum temperatures in
Bettles (Fig. 1) are 20.18C (SD ¼ 5.6) and 20.88C (SD
¼ 1.8), respectively; mean annual precipitation is 360
mm (SD ¼ 94), with 55% falling between June and
September (Western Regional Climate Center, 1951–
2007 observations; available online).6 Forests and
woodlands dominate lowlands and hill slopes in the
study region, with Picea mariana in wet muskegs, P.
glauca (Moench) Voss. and Populus balsamifera Mill.
along riparian areas, and P. glauca, Betula papyrifera
Marsh. and Populus tremuloides Michx. on uplands and
warm, south-facing slopes (Nowacki et al. 2000). Salix
spp., Betula glandulosa Michx., and Alnus spp. form
shrub communities in non-forested areas (Nowacki et al.
2000). Fire is the primary disturbance agent in the
region, with an estimated fire rotation period of 175
years (based on observations from 1950 to 2001 from the
Kobuk Ridges and Valleys Ecoregion; Kasischke et al.
2002).
We cored lakes 2–15 ha in size and 7.0–11.6 m deep
(Table 1), which are currently surrounded by discontin-
uous P. mariana-dominated forest. Recent fires burned
to the edge of Ruppert Lake (RP) in Common Era (CE)
1991 (15 357 ha), to 1 km and 3 km east of Code Lake
(CO) in CE 1959 (788 ha) and 1949 (2456 ha), and to 5
km west and 1 km southwest of Wild Tussock Lake
(WK) in CE 1997 (9750 ha) and 1991 (6390 ha; Fig. 1;
Alaska Fire Service 2004).
METHODS
Lake sediments
Sediments were collected from the center of each lake
with two parallel, overlapping 8-cm diameter cores in
summer 2001 (CO), 2002 (RP), or 2003 (XI, WK), using
a modified Livingstone piston corer (Wright et al. 1984).
Surface sediments (roughly ,50 cm) were collected with
a polycarbonate tube and the top 10–20 cm sliced at 0.5–
1.0 cm in the field. All cores had intermittent laminae,
which were used to match records from overlapping
segments of adjacent cores. Sediments .10–20 cm depth
6 hhttp://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ak0761i
PHILIP E. HIGUERA ET AL.202 Ecological Monographs
Vol. 79, No. 2

were sliced at 0.25 cm to 0.50 cm intervals, and 1-cm3
subsamples were prepared at varying intervals for pollen
and stomata analysis according to PALE protocols
(PALE members 1994), except that samples were not
subjected to a coarse sieve so as not to remove any
stomata (Carlson 2003, Pisaric et al. 2003). Pollen was
counted at 400–10003 magnification to a terrestrial
pollen sum . 300 (mean¼ 398, SD¼ 107) and displayed
as percentages of total terrestrial pollen. In samples
bracketing the Picea pollen rise ca. 5500 yr BP at
selected sites (Brubaker et al. 1983), (1) pollen slides
were scanned for Picea stomata, identified based on
comparison with an Alaskan reference collection and
Hansen (1994), while counting Lycopodium spores to an
equivalent pollen sum of 2000 grains (Carlson 2003),
and (2) Picea pollen grains were classified as P. mariana
or P. glauca based on morphological measurements on
;30 Picea pollen grains per sample (Appendix A). For
charcoal identification, 3–5-cm3 subsamples were taken
from contiguous core slices and prepared following
Higuera et al. (2005). Charcoal was identified at 10–403
magnification based on color, morphology, and texture.
FIG. 1. Location of lakes in this study and others discussed in the text (1, Dune Lake; 2, Low Lake; 3, Moose Lake; 4,
Chokosna Lake; 5, Paradox Lake). Gray polygons are areas that have burned between Common Era (CE) 1950 and 2003 (Alaska
Fire Service 2004), and the dashed line on the lower map is the southern border of Gates of the Arctic National Park. The black
dots and larger circles identifying each lake on the bottom map have 1- and 2-km diameters, representing the approximate spatial
scale of each fire-history record.
May 2009 203BROOKS RANGE FIRE HISTORY

Charcoal concentrations (pieces per cubic centimeter)
were multiplied by the estimated sedimentation rate
(centimeters per year) to obtain the charcoal accumula-
tion rate (CHAR; number of pieces per square
centimeter per year) of each sample. Quantifying
charcoal pieces instead of area was supported by
empirical and theoretical studies linking fires with high
charcoal counts (Whitlock and Millspaugh 1996, Gard-
ner and Whitlock 2001, Gavin et al. 2003, Higuera et al.
2005, Peters and Higuera 2007) and by strong correla-
tions between charcoal area and charcoal counts within
individual records (Hallett and Walker 2000, Hallett et
al. 2003). By using charcoal counts, we implicitly
assumed that fragmentation of charcoal pieces was
constant through time.
Chronologies
Sediment chronologies for each site were based on
210Pb dates for the upper 10–20 cm (using the CRS
model; Binford 1990) and/or on AMS 14C ages of
concentrated charcoal from charcoal peaks, concentrat-
ed Picea pollen, or terrestrial macrofossils for deeper
sediments. Age models were developed individually for
the 210Pb and 14C portions of each core using a weighted
cubic smoothing spline in Matlab (MathWorks 2005)
taking into account the number and uncertainty of age
estimates. The number of age estimates in a chronology
determined the smoothing parameter for each spline,
such that a larger number of ages for a given time
interval resulted in a more flexible spline. The uncer-
tainty of each age estimate (i.e., two standard devia-
tions) was used to weight the influence of each age in the
age–depth model (cf. Telford et al. 2004). Finally,
confidence intervals for each age–depth model, reflecting
the combined uncertainty of all age estimates in a model,
were derived from 1000 bootstrapped chronologies. For
each bootstrapped chronology, each age used to develop
the chronology was selected randomly based on the
probably distribution of the 210Pb or calibrated 14C date.
The final chronology represents the median age at each
depth from the 1000 bootstrapped chronologies.
Analysis of pollen and stomata data
Pollen zones were delineated primarily by visual
inspection of pollen percentages of major tree, shrub,
and herb taxa (i.e., Cyperaceae, Betula, Populus, Picea,
Alnus) using the midpoint of the pollen increase or
decrease of a taxon to establish a zone boundary. In
addition, we used a modern analog analysis based on
squared-chord distances (SCD) and receiver operating
characteristic curves to quantify the probability that
fossil pollen assemblages resembled modern pollen
assemblages from North American Arctic Tundra,
Boreal Forest, and Forest–tundra biomes (Appendix
A). The arrival of P. mariana and development of the
modern boreal forest was estimated via (1) Picea
stomata presence/absence (RP and WK), (2) discrimi-
nant analysis (RP and WK) of Picea pollen grains, and
(3) modern analog analysis (Appendix A). This ap-
proach yielded pollen zones and vegetation interpreta-
tions similar to those previously recognized in the region
(Anderson and Brubaker 1994).
Statistical treatment of charcoal records
To assess whether CHAR differed between past
biomes, we compared CHAR distributions between
pollen zones in each lake using a two-sample Kolmog-
orov-Smirnov (K-S) test (e.g., Clark 1990, Lynch et al.
2002). Prior to subsequent analyses, we interpolated
CHARs to 15-yr time steps (Cint), approximating the
mean sampling resolution at all sites (Table 1). This
procedure combines samples based on their proportion-
al contribution to each time step. In instances where
sample resolution was.15 yr, the nature of the record is
not altered, but when sample resolution is ,15 yr, some
variations are lost (Long et al. 1998, Carcaillet et al.
2001, Gavin et al. 2006). This step was necessary to
reduce biases in the ability to detect fires due to variable
sample resolution within and between records.
We inferred the timing of ‘‘local’’ fires by decompos-
ing charcoal records (e.g., Clark et al. 1996) to identify
distinct charcoal peaks based on a standard set of
threshold criteria applied to all records. Consistent with
empirical and theoretical studies (Lynch et al. 2004a,
Higuera et al. 2007), we use the term ‘‘local’’ to refer to
distances within ;500–1000 m of each lake, correspond-
ing to an area of ;100–300 ha (1–3 km2). We assumed
that low-frequency variations in CHAR (background),
Cback, reflect changes in the rate of total charcoal
production, secondary charcoal transport, and sediment
mixing (Clark et al. 1996, Long et al. 1998, Higuera et al.
2007). We estimated Cback by calculating a 500-yr
moving median (XI, CO, WK) or mode (RP, because
this yielded a higher signal-to-noise index; see end of this
TABLE 1. Lake locations, characteristics, and record quality.
Lake name
(unofficial)
Latitude
(N)
Longitude
(W)
Elevation
(m above
sea level)
Surface
area (ha)
Age of
record
(ka)
Sedimentation rate
(cm/yr; mean 6 SD)
Resolution
(yr/sample;
mean 6 SD)
Median
signal-to-noise
index
Ruppert 6780401600 15481404500 230 3 14.0 0.040 6 0.023 13 6 6 0.88
Xindi 6780604200 15282903000 240 7 15.5 0.025 6 0.016 32 6 24 0.71
Code 6780902900 15185104000 250 2 7.5 0.017 6 0.003 16 6 3 0.83
Wild Tussock 6780704000 15182205500 290 15 8.0 0.019 6 0.001 14 6 5 0.84
 Mean (6SD) sample resolution and the signal-to-noise index for the section of core used for charcoal peak identification were
23 (613) years and 0.83, respectively.
PHILIP E. HIGUERA ET AL.204 Ecological Monographs
Vol. 79, No. 2

section) across each record, and then smoothing this
series with a locally weighted regression using a 500-yr
window. Cback was subtracted from Cint to obtain a
residual series, Cpeak (i.e., Cpeak ¼ Cint  Cback), which
contains high-frequency variability around the long-
term trend described by Cback (e.g., Clark et al. 1996).
Previous decomposition methods have assumed a
constant relationship between Cpeak and Cback and
applied a globally defined threshold value to Cpeak series
to identify charcoal peaks. Because the variability of
Cpeak around Cback changes through time in our records
(Fig. 2), we developed a novel approach to identify
peaks using a locally defined threshold based on
variability around each sample. Specifically, we used a
Gaussian mixture model to separate Cpeak values within
each overlapping 500-yr portion of a record into two
components: (1) Cnoise, variations around Cback that
reflect natural and analytical effects (e.g., sediment
mixing, sediment sampling), and (2) Cfire, variations
exceeding variability in the Cnoise distribution, assumed
to reflect the occurrence of one or more local fires (‘‘fire
events’’; Clark et al. 1996, Gavin et al. 2006). Because
the threshold separating Cfire from Cnoise should occur in
the upper range of the Cnoise population, we considered
three possible threshold values corresponding to the
95th, 99th, and 99.9th percentile of the Cnoise population
for every 500-yr portion of the record. We present
results from all three criteria but discuss only those using
the 99th percentile criterion. Finally, all peaks identified
were screened to test whether variations between a
‘‘peak’’ and the smallest ‘‘non-peak’’ sample within the
previous five samples (i.e., 75 yr) differed statistically
based on the original charcoal counts and sample
volume (Gavin et al. 2006; Appendix A). This screening
eliminates ‘‘peaks’’ when variations in CHAR are based
on small differences in charcoal counts. Our methods are
described in detail in Appendix A and in the publicly
FIG. 2. Selected local distributions of the peak charcoal series, Cpeak, from Ruppert Lake (gray bars). Each panel represents a
500-year, nonoverlapping section of the record and includes the two modeled Gaussian distributions (and is thus analogous to
panels c and d in Fig. 6 of Gavin et al. 2006, but for distinct portions of the Ruppert Lake record). All samples above the threshold
value, ti (vertical line), represent charcoal from local fires, Cfire. The threshold cuts off 99% of the samples assumed to represent
natural and analytical noise, Cnoise distribution (black line with lower mean). This procedure is repeated for every sample in the
record, resulting in a unique threshold value reflecting the variability in Cnoise around each sample. Note that the distribution of
Cnoise varies throughout the record, and thus, ti and the signal-to-noise index (SNI, see Methods) also vary. ‘‘KS P’’ is the P value
resulting from a two-sample Kolmogorov-Smirnoff goodness-of-fit test between the empirical Cnoise values and the modeled Cnoise
distribution, thus providing an index of how well the Cnoise model fits the empirical data.
May 2009 205BROOKS RANGE FIRE HISTORY

available program CharAnalysis (written by P. E.
Higuera; available online).7
In addition, as an indication of the suitability of
charcoal records for peak identification, we calculated a
signal-to-noise index (SNI) for each sample, i, which
describes the variance within the Cfire distribution (i.e.,
signal) relative to the total variance in Cpeak in the 500
years surrounding that sample:
SNIi ¼
varðCfire;iÞ
varðCnoise;iÞ þ varðCfire;iÞ
:
The SNI varies from 0 to 1, with high values repre-
senting large separation between charcoal peaks and
non-peaks, and values near zero represent little separa-
tion between peaks and non-peaks.
Quantifying and detecting differences in fire regimes
We inferred aspects of past fire regimes based on the
magnitude and temporal pattern of identified charcoal
peaks. Peak magnitude, the number of charcoal pieces
from all samples defining a given peak (i.e., all samples
above the threshold value; number of pieces per square
centimeter per peak), is a measure of total charcoal
deposition per fire event (Whitlock et al. 2006).
Systematic changes in peak magnitude at millennial
time scales were used as a qualitative proxy for average
fuel consumption per fire, which should reflect fire size
and/or fuel consumption for a given area burned.
Interpretations rest on theoretical relationships between
fire and charcoal deposition at a lake (Higuera et al.
2007), although links between fuel consumption and
peak magnitude have yet to be tested empirically. For a
given fire, charcoal deposition at a lake varies based on
location, size, and charcoal production (i.e., fuel
consumption). When a large number of fires are
recorded, variations in peak magnitude due to fire
location contribute little to the long-term pattern, which
should thus reflect relative changes in charcoal produc-
tion.
We used the distribution of fire return intervals (years
per fire; FRIs) within each pollen zone to characterize
the temporal characteristics of fire regimes for each
vegetation zone. FRI distributions were described by the
mean FRI (FRImean), although the median FRI
(FRImedian) is also reported in tabular form. If a pollen
zone had .5 FRIs (.6 fires), a two-parameter Weibull
model was fit to FRIs using maximum-likelihood
techniques (in Matlab; Clark 1989, Johnson and Gutsell
1994, MathWorks 2005). Goodness of fit for each
Weibull model was tested with a one-sample K-S test
(Zar 1999); Weibull models are not reported unless P .
0.10 (i.e., there was .10% chance that the empirical
distribution was not different from the Weibull model;
Johnson and Gutsell 1994). Confidence intervals (95%)
for Weibull parameters, FRImean, and FRImedian were
estimated based on 1000 bootstrapped samples from
each distribution.
We tested two null hypotheses using a likelihood-ratio
test (LRT) based on estimates of the Weibull b and c
parameters (Appendix A): (1) FRIs did not differ
between pollen zones within a given site, and (2) FRIs
did not differ between sites within a given pollen zone.
By utilizing both parameters of the Weibull distribution,
the LRT provides a more powerful method for detecting
difference in FRI distributions than possible by inter-
preting confidence intervals around the mean or median
FRI and estimated Weibull statistics (e.g., Clark 1990,
Lynch et al. 2002) or by using the nonparametric K-S
test (Lynch et al. 2002, Anderson et al. 2006, Gavin et al.
2006). We rejected the null hypothesis if P  0.05. If
FRI distributions within a single vegetation zone were
statistically similar across sites (i.e., null hypothesis 2
was not rejected), we pooled FRIs to form a composite
record representing FRIs from across the study area
(i.e., two to three sites). With the pooled FRIs we
performed the same between-zone comparisons as with
individual records. Due to the increased sample size in
the composite records, this procedure yields greater
statistical power for detecting differences between fire
regimes in different vegetation zones. Because, in most
cases, comparisons between zones at individual sites
were similar to results for the pooled data (although not
always significant), we focus our discussion on results
from the pooled data.
RESULTS AND PALEOVEGETATION INTERPRETATIONS
Chronologies and sedimentation rates
The Ruppert (RP) and Xindi (XI) records are older
(starting ca. 14 000 and 15 500 yr BP) than the Code
(CO) and Wild Tussock (WK) records (starting ca. 7500
and 7800 yr BP; Fig. 3). At all lakes, age models since
8000 yr BP were well constrained and generally pass
through the 95% confidence interval of 14C or 210Pb
dates (Fig. 3). At RP, we did not use two 14C dates on
concentrated pollen (19.02 and 29.50 cm) because they
were ;500–1000 years older than ages defined by five
other 14C dates on charcoal in sediments spanning these
core depths (10–60 cm; Fig. 3, Appendix B). At RP and
XI, age models .8000 yr BP were less well constrained,
and predicted ages do not always intersect the uncer-
tainty of 14C dates (e.g., RP; Fig. 3). Given these results
and the sensitivity of CHAR to sedimentation rate, we
evaluated whether different choices of age–depth rela-
tionships altered the general features of the CHAR series
at these sites. We developed five to seven alternative
age–depth relationships by excluding individual dates
and changing the age–depth model criteria. In no case
did the overall nature of the CHAR records change.
Sedimentation rates ranged from 0.017 to 0.040 cm/yr
(Table 1, Fig. 3) and varied little in the CO and WK
records, but were higher prior to ca. 8000 and 11 000 yr
BP at RP and XI, respectively (Fig. 3). The mean sample
resolution at each site was ;15 yr/sample (Table 1) and7 hhttp://CharAnalysis.googlepages.comi
PHILIP E. HIGUERA ET AL.206 Ecological Monographs
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varied from ,5 to ;50 yr/sample (Fig. 3). Slow
sedimentation resulted in low sample resolution at XI
from 8000–0 yr BP (.50 yr/sample).
Peak identification in charcoal records
The median signal-to-noise index (SNI) for all records
where peak analysis was interpreted exceeded 0.80
(Table 1; e.g., Ruppert Lake, Fig. 2). This is well above
the median SNI expected from records without a peak
signal (e.g., 0.15 for red noise; Appendix A) and
indicates good separation between peak and non-peak
values. We did not interpret peak analysis results at XI
from 8000–0 yr BP because the SNI in this period was
consistently ,0.5 (data not shown). The sensitivity of
charcoal peak identification to different threshold
criteria varied between pollen zones and between sites
(Fig. 4), but characterizations of FRI distributions were
generally insensitive to all three threshold criteria (data
not shown).
Comparisons between known fire events and the most
recent charcoal peaks at RP, CO, and WK support the
assumption that identified charcoal peaks detect fires
within (and not beyond) 1 km of these lakes. The CE
1991 (41 yr BP) fire that burned to the edge of RP was
represented by an identified peak centered at39 yr BP,
while the most recent peaks identified at CO (69 yr BP)
and WK (38 yr BP) both occur before the start of fire
observation in CE 1950 (0 yr BP); thus, the post-1950
fires that burned to ;1, 3, and 5 km from these lakes
were not detected by the charcoal record.
Pollen, stomata, and charcoal records
Herb Tundra Zone, 14 000–13 300 (RP), 15 500–14 300
(XI) yr BP.—This zone was characterized by Cyperaceae
(.25%), Salix (;25%), Poaceae (;15%), and Artemisia
(;10%) pollen, with relatively high percentages of
Pediastrum algal cell nets (.25%; Fig. 5, Appendix B).
Although SCD was lowest for comparisons with Arctic
Tundra (;0.2), the probability of analog (,20%)
indicates little similarity with modern tundra (Fig. 2,
Appendix B). Our pollen results are consistent with
previous studies in the region (Anderson et al. 1989,
Anderson and Brubaker 1994) and suggest the presence of
a discontinuous prostrate shrub tundra and grass–forb
tundra (see Anderson et al. 2004). CHARs were low at
both sites (medians¼ 0.00–0.01 piecescm2yr1; Appen-
dix B). The presence of only one identified charcoal peak
(at RP; Fig. 4) precludes the analysis of fire regimes but
suggests long FRIs in this zone.
Shrub Tundra Zone, 13 300–10 300 (RP), 14 300–
10 300 (XI) yr BP.—Increased Betula pollen, interpret-
ed as B. glandulosa and/or B. nana (Anderson and
Brubaker 1994), to .60% marks the transition from
herb to shrub tundra (Fig. 5, Appendix B). SCD (;0.2)
continues to indicate a low similarity of fossil to modern
pollen assemblages from all modern biomes (probability
of analog ,20%; Fig. 5, Appendix B). Betula pollen
percentages higher than in modern tundra communities
suggest that the landscape was covered by relatively
dense thickets of tall (.1 m) birch shrubs (Brubaker et
al. 1983, Anderson and Brubaker 1993). CHARs
increase at the onset of this zone (medians ¼ 0.02–0.05
piecescm2yr1), and CHAR distributions were distinct
from those in the Herb Tundra Zone (P , 0.01;
Appendix B). Maximum peak magnitudes exceed 5
piecescm2peak1 (Fig. 4). Fire regimes at RP and XI
were characterized by a FRImean (95% CI) of 137 (107–
171) and 150 (115–186) yr, respectively (Table 2, Fig. 6),
with no difference in FRI distributions between sites
(Table 3, Fig. 6). The FRImean (95% CI) of the pooled
record was 144 (120–169) yr (Fig. 7).
Deciduous Woodland Zone, 10 300–8500 (RP),
10 300–8000 (XI) yr BP.—This zone was characterized
by increased Populus pollen percentages (10%–20%; Fig.
5, Appendix B) and was inferred to represent woodland
of P. balsamifera and P. tremuloides (Anderson et al.
2004, Edwards et al. 2005). Limited macrofossil evidence
(Edwards et al. 2005) also suggests the possibility of
tree-size Betula within this period. SCD was the highest
for the entire record (.0.3), and no analogs exist with
modern North America pollen spectra (probability of
analog ,0.2; Fig. 5, Appendix B). CHARs decrease
(medians¼ 0.01–0.02 piecescm2yr1) and distributions
were distinct from those in the Shrub Tundra Zone (P ,
0.01; Appendix B). Peak magnitudes decrease to ,5
piecescm2peak1 (Fig. 4). The FRImean (95% CI) at
RP and XI was 223 (90–390) yr and 293 (225–360) yr,
respectively (Table 2, Fig. 6). Because XI recorded only
four FRIs (Table 3, Fig. 6), we deemed the FRImean
between RP and XI sufficiently close to pool data from
within this zone. The FRImean (95% CI) of the composite
record was 251 (156–347) yr, significantly different from
the composite record of the Shrub Tundra Zone (P ¼
0.03; Fig. 7, Appendix B).
Forest–tundra Zone, 8500–5500 (RP), 8000–5500
(XI), 7500–5500 (CO), 7800–5500 (WK) yr BP.—
Decreased Populus (,10%) and increased in Picea
(1%, ,10%) pollen percentages mark the onset of the
Forest–tundra Zone. Discriminant analysis of Picea
pollen at RP and WK (Appendix B) and previous
research (Brubaker et al. 1983), indicate the nearly
exclusive presence of P. glauca in this zone. Alnus pollen
percentages increased from trace amounts to .50%
starting around 7250–7500 yr BP, coinciding with the
start of the CO and WK records (Fig. 5, Appendix B).
With the rise in Alnus pollen, SCD decreases for
comparisons to modern Boreal Forest and Forest
Tundra (,0.1), and probability-of-analog for these
biomes increase to .30%–40% (Fig. 5, Appendix B).
Together, pollen and stomata data suggest that the
vegetation resembled modern treeline, with P. glauca
trees or stands dispersed within a landscape of Betula or
Betula and Alnus shrubs (Fig. 5, Appendix B).
CHARs at XI (median ¼ 0.03 piecescm2yr1) were
higher than in all previous zones (P , 0.01), while
CHARs at RP were intermediate between the Herb
May 2009 207BROOKS RANGE FIRE HISTORY

Tundra (P , 0.01) and Shrub Tundra Zones (P , 0.01;
medians ¼ 0.02–0.03 piecescm2yr1; Appendix B).
Charcoal peak magnitudes were similar to the previous
zone at RP and generally remain below 5 piece-
scm2peak1 at all sites (Fig. 4). FRImean (95% CI)
for RP, CO, and WK were 238 (158–324), 210 (145–
277), and 131 (95–172) yr, respectively. Except for RP
vs. WK (P ¼ 0.04), FRI distributions did not differ
among sites (Table 3, Fig. 6). Given the statistical
difference between RP and WK, the composite FRI
distribution only includes FRIs from RP and CO. The
composite record had a FRImean (95% CI) of 227 (170–
287) yr, similar to the composite record from the
Deciduous Woodland Zone, but significantly longer
than the composite records from the Shrub Tundra
Zone (P ¼ 0.02; Fig. 7, Appendix B).
Boreal Forest Zone, 5500 yr BP–present (RP, XI, CO,
WK).—Picea pollen percentages increased to .10% at
all sites between 6000 and 4000 BP (Fig. 5, Appendix B)
and indicate the development of the modern boreal
forest (Anderson and Brubaker 1994). With the rise in
Picea pollen, all sites show an increase in the probabil-
ity-of-analog with the modern Boreal Forest biome
(.75%) and lower probabilities for modern Forest–
tundra (roughly ,70%; Fig. 5, Appendix B). The first
presence of Picea stomata ca. 5000 yr BP (RP; Fig. 5)
FIG. 3. Age–depth models for each site with the resulting sedimentation rates and sample resolution, with 95% confidence
intervals. At Ruppert Lake, temporal resolution changes from ;25–10 yr/sample at 2200 BP because sampling intervals change
from 0.5 cm to 0.25 cm. See Results for explanation of the excluded dates for Ruppert Lake.
PHILIP E. HIGUERA ET AL.208 Ecological Monographs
Vol. 79, No. 2

and 5400 yr BP (WK; Appendix B) coincides with the
transition from Forest–tundra to Boreal Forest inferred
from the modern analog analysis (Fig. 5, Appendix B),
Picea pollen morphology from RP and WK (Appendix
B), and previous research (Brubaker et al. 1983),
indicating an increase of P. mariana at this time
(Appendix B). While the beginning age of this zone
differed across sites (e.g., RP vs. CO and WK; Fig. 5,
Appendix B), our statistical comparisons of FRI
distributions were insensitive to starting date of 5000
vs. 5500 yr BP (data not shown). We used 5500 yr BP at
all sites for simplicity.
CHARs increased to their highest level in most
records (median CHAR ¼ 0.05–0.11 piecescm2yr1;
Fig. 4, Appendix B). FRImean (95% CI) at RP, CO, and
WK were 171 (135–216), 135 (113–160), and 135 (113–
157) yr, respectively (Table 2, Fig. 6), and FRI
distributions did not differ between sites (Table 3, Fig.
6). The FRImean (95% CI) of the composite record was
145 (130–163) yr; significantly shorter than the FRImean
in the composite record from the Forest–tundra and
Deciduous Woodland Zones, but similar to the com-
posite record from the Shrub Tundra Zone (Fig. 7,
Appendix B). To test the sensitivity of our results to
FRIs from WK, we constructed a pooled record for the
Boreal Forest Zone that excluded WK. Significant
differences remained when comparing the pooled
Forest–tundra and Boreal Forest Zones (P ¼ 0.05), but
not when comparing pooled Deciduous Woodland and
Boreal Forest Zones (P ¼ 0.07).
FIG. 3. Continued.
May 2009 209BROOKS RANGE FIRE HISTORY

DISCUSSION
Interpreting sediment charcoal records
and detecting changes in fire regimes
We introduce three general tools that facilitate the
interpretation of fire history from sediment-charcoal
records. First, the signal-to-noise index provides a semi-
objective way to judge if a record is appropriate for peak
analysis. For example, while .0.8 in most records, SNI
values were consistently,0.5 for the 8000–0 yr BP in the
Xindi Lake record (data not shown), indicating that this
section was not suitable for peak identification. Second,
our use of a Gaussian mixture model to determine
threshold values for peak identification allowed us to
treat all charcoal records with one set of semi-objective
criteria. These criteria are consistent with a mechanistic
FIG. 4. Charcoal records for (a) Ruppert, (b) Xindi, (c) Code, and (d) Wild Tussock lakes. (i) Interpolated charcoal
accumulation rates (CHAR), Cint (black), and background CHAR, Cback (gray); (ii) Peak CHAR, Cpeak, with the values identifying
noise-related variability (positive and negative gray lines) and peaks identified with each threshold criterion. The 99th percentile
criterion used for interpretation is represented withþ, and the 95th and 99.9th percentile results are represented with gray dots. (iii)
Pollen-inferred vegetation zone and peak magnitude for all charcoal accumulation rate (CHAR) values exceeding the positive
threshold value in panels ii. Note peak magnitude values not corresponding toþ symbols in panels ii are those that did not pass the
minimum-count screening (see Methods for details), andþ symbols in panels ii with no apparent peak magnitude value correspond
to very small peak magnitudes.
PHILIP E. HIGUERA ET AL.210 Ecological Monographs
Vol. 79, No. 2

model of the origin of charcoal records (Higuera et al.
2007) and have the advantage of being established a
priori. Further, because thresholds are defined locally,
this approach is appropriate for records with changing
variability in charcoal accumulation and is insensitive to
variety of analytical choices (e.g., transforming charcoal
data, defining Cpeak via ratios vs. residuals; P. E.
Higuera, unpublished data). Although a robust calibra-
tion requires a large data set of known fires, our
approach appears successful, as the 99th-precentile
criterion accurately identified known fires within one
km of each lake. Third, using the likelihood-ratio test
and pooling FRIs from multiple records greatly im-
proved the ability to detect changes in fire regimes with
long return intervals. In stand-replacing fire regimes with
long and variable FRIs, individual records can detect
only large or long-lasting changes in FRImean (e.g.,
.30%–50% change over millennial time scales). Pooling
data from several sites increased the sample size of FRIs
in this study and allowed the detection of statistical
differences between pollen zones that were not possible
using single records (e.g., Ruppert Lake record; Fig. 6).
This approach assumes that fire regimes are homoge-
nous across sites and within time periods. We used
FIG. 4. Continued.
May 2009 211BROOKS RANGE FIRE HISTORY

between-site comparisons to evaluate the assumption of
homogenous fire regimes, leading to the elimination of
sites with statistically different records from the pooled
data set (e.g., WK within the Forest–tundra Zone).
Late-glacial and Holocene fire regimes:
patterns and inferred controls
Herb Tundra Zone.—Though our records span a brief
portion of this zone, the charcoal series suggest that fire
was rare in the late-glacial herb tundra. Both climate
and vegetation change likely reduced the probability of
fire. Summers were cooler and drier than present
(Anderson and Brubaker 1994, Edwards et al. 2001,
Anderson et al. 2004), with cold temperatures implying
limited fuel drying and limited convection necessary for
lightning ignition. While species composition differed
from modern tundra, the structure of vegetation in this
zone may have been similar to present high-arctic
tundra, where a cold and dry climate results in
discontinuous vascular plant cover (Walker et al. 2005)
that supports few fires (Kasischke et al. 2002).
Shrub Tundra Zone.—Fire activity increased marked-
ly with the transition from herb to shrub tundra ca.
13 300–14 300 yr BP, resulting in a FRImean (144 yr [120–
169]; Fig. 7) statistically similar to those of modern
Alaskan boreal forests (Figs. 6 and 7; Kasischke et al.
2002, Lynch et al. 2002). These short FRIs contrast
sharply with those of modern Alaskan tundra, as only
;3% of arctic tundra burned between CE 1950 and 2004
(Alaska Fire Service 2004, Walker et al. 2005) and even
the most flammable tundra region, the Seward Peninsula
(Fig. 1), has an estimated fire rotation period (analogous
to a FRImean) of 270 yr (Kasischke et al. 2002). Lower
overall CHARs and peak magnitudes (Fig. 4, Appendix
FIG. 5. Paleovegetation and charcoal data from Ruppert Lake. From top to bottom: pollen and spore percentages of selected
taxa; total pollen accumulation rate (PAR); squared chord distance (SCD) and probability of analog values for comparisons
between fossil samples and those from modern Boreal Forest, Forest–tundra, and Arctic Tundra vegetation zones; and charcoal
accumulation rate (CHAR). Solid and open circles on Picea panel represent Picea stomata presence and absence, respectively.
Triangles below lower horizontal axis represent the location of 14C or 210Pb dates. See Appendix B for the same figures for Xindi,
Code, and Wild Tussock Lakes.
PHILIP E. HIGUERA ET AL.212 Ecological Monographs
Vol. 79, No. 2

B) in the Shrub Tundra Zone also suggest that fuel
consumption (i.e., biomass burned per fire) was lower
than in modern boreal forests.
We suggest elsewhere (Higuera et al. 2008) that a
change in tundra fuel characteristics was the primary
driver of increased fire activity in the Shrub Tundra Zone.
Although summer temperatures increased between the
Herb and Shrub Tundra zones, temperatures remained
cooler than present (Anderson and Brubaker 1994,
Edwards et al. 2001, Anderson et al. 2004), making it
unlikely that temperature alone caused fire frequencies to
be similar to modern boreal forests (Table 3, Fig. 4).
Similarly, changes in moisture were unimportant, as
moisture remained similar to or increased slightly from
TABLE 2. Fire regime statistics for each site, stratified by pollen-defined vegetation zone.
Site and zone NFRI
Fire history parameter (95% CI)
Mean fire return
interval (yr)
Median fire return
interval (yr)
Weibull b
parameter (yr)
Weibull c
parameter (unitless)
Ruppert
Shrub Tundra 20 137 (107–171) 128 (90–180) 151 (116–191) 1.84 (1.43–3.35)
Deciduous Woodland 6 223 (90–390) 158 (53–458) 229 (94–429) 1.16 (0.90–3.11)
Forest–tundra 12 238 (158–324) 210 (90–383) 262 (172–360) 1.63 (1.30–2.75)
Boreal Forest 31 171 (135–216) 120 (98–173) 188 (147–239) 1.53 (1.31–2.06)
Xindi
Shrub Tundra 24 150 (115–186) 113 (90–173) 164 (122–207) 1.68 (1.45–2.25)
Deciduous Woodland 4 293 (225–360) 292 (225–360) Weibull model not fit (,5 FRI)
Code
Forest–tundra 8 210 (145–277) 195 (105–315) 235 (162–302) 2.39 (1.78–5.20)
Boreal Forest 39 135 (113–160) 135 (105–150) 150 (123–178) 1.85 (1.52–2.60)
Wild Tussock
Forest–tundra 16 131 (95–172) 105 (60–180) 145 (104–191) 1.66 (1.38–2.50)
Boreal Forest 39 135 (113–157) 135 (105–165) 149 (123–174) 1.96 (1.61–2.75)
Notes: Parentheses enclose 95% confidence intervals estimated by 1000 bootstrapped samples of the fire return interval (FRI)
distributions. NFRI is the number of fire return intervals in each zone (the total number of fires in a zone, minus 1).
FIG. 6. Distribution of fire return intervals (FRIs) and fitted Weibull models for each vegetation zone (columns) at each site
(rows). Results from statistical comparisons are summarized by¼, similar (P . 0.05), or 6¼, not similar (P  0.05). Table 3 contains
P values for all comparisons. Panel (f ) has too few intervals (,5 FRIs) to compare to the other populations.
May 2009 213BROOKS RANGE FIRE HISTORY

theHerb Tundra Zone (Anderson et al. 2004). In contrast,
vegetation flammability changed dramatically with the
expansion of B. glandulosa, which has highly resinous
stems (Dugle 1966), burns readily (de Groot and Wein
2004), and resprouts well after fires (de Groot and Wein
1999). We propose, therefore, that increased landscape
flammability was the primary driver of increased burning
during the Shrub Tundra Zone. This inference is also
compatible with the hypothesis that the extinction of
grazing megafauna (e.g., Equus, Mammuthus) and the
arrival of humans was the primary driver of increased
shrub cover (Guthrie 2006). The co-occurring changes in
climate, vegetation, megafauna, and human populations
makes this period unusually complex ecologically.
Deciduous Woodland Zone.—With the development of
deciduous woodlands ca. 10 500 yr BP, fires became less
common (FRImean 251 yr [156–347]; Fig. 7) and
produced little charcoal (Fig. 4, Appendix B). Given
regional evidence that summers that were 18–28C warmer
and 25%–40% drier than present (Edwards et al. 2001,
Anderson et al. 2004, Kaufman et al. 2004), one would
predict an increase, rather than decrease, in fire activity
during this period. Though inconsistent with direction of
climate change, the decline in fire during this period is
consistent with the lower flammability of deciduous trees
and their tendency to act as fire breaks in modern boreal
forests (Johnson 1992, Cumming 2001, Hely et al. 2001).
Thus, one scenario for the decrease in fire occurrence in
this zone is that deciduous trees reduced fire spread
across the landscape. The association of Populus pollen
and decreased fire occurrence in our records differs from
studies from southern Alaska (Anderson et al. 2006) and
eastern Canada (Richard et al. 1992, Carcaillet and
Richard 2000), which inferred high fire occurrence
during early Holocene periods with a presence of
Populus. For example, at Paradox Lake (Fig. 1) on the
Kenai Peninsula, Alaska, Anderson et al. (2006) infer
that fires were common (FRImean of 77 yr,649 SD) when
Populus pollen was abundant (8500–10 700 BP). Though
it is difficult to reconcile the short FRIs at Paradox Lake
with the low CHAR in the Paradox record, this finding
suggests that Populus itself does not preclude frequent
burning. Climatic difference between the Kenai Penin-
sula and Brooks Range may have resulted in different
ignition rates and moisture levels during this period. The
differences in studies, plus an alternative climatic
interpretation of increased moisture during the Decidu-
ous Woodland Zone (Anderson and Brubaker 1993),
suggest the possibility that both climate (via increased
moisture) and vegetation lower the probability of fire.
TABLE 3. Probability of Type I error for within-site, between-zone (italic), between-sites, within-zone (boldface), and between-site,
between-zone (non-boldface type) comparisons of fire-return-interval distributions based on the likelihood-ratio test.
Zone and site ST, XI DW, RP FT, RP FT, CO FT, WK BF, RP BF, CO BF, WK
ST, RP (20) 0.60 0.10 0.05* 0.15 0.89 0.29 0.99 0.96
ST, XI (24) 0.18 0.10 0.18 0.73 0.59 0.58 0.33
DW, RP (6) 0.65 0.23 0.15 0.44 0.05* 0.02*
FT, RP (12) 0.46 0.04* 0.28 0.02* 0.00*
FT, CO (8) 0.09 0.40 0.15 0.07
FT, WK (16) 0.41 0.92 0.75
BF, RP (31) 0.17 0.11
BF, CO (39) 0.92
BF, WK (39)
Notes: There were no fire return intervals (FRIs) in the Herb Tundra Zone and only four FRIs in the Deciduous Woodland Zone
at Xindi Lake; thus, these zones were not compared. Zone abbreviations are: Shrub Tundra (ST), Deciduous Woodland (DW),
Forest–tundra (FT), and Boreal Forest (BF). Site abbreviations are: Ruppert (RP), Xindi (XI), Code (CO), Wild Tussock (WK).
Numbers in parentheses are the sample sizes for each zone.
* P  0.05.
FIG. 7. Results from the analysis of pooled fire return intervals (FRIs). For each vegetation zone, we show distributions of
FRIs, fitted Weibull models, Weibull b and c parameters (95% CI), median and mean fire return intervals (FRImedian, FRImean; 95%
CI), and number of FRIs in each vegetation zone. Results from statistical comparisons are summarized as in Table 3 and Fig. 6.
Note that Wild Tussock Lake (WK) is not included in the pooled record for the Forest–tundra Zone because of statistical
differences between WK and Ruppert Lake (RP) during this period (Table 3, Fig. 6).
PHILIP E. HIGUERA ET AL.214 Ecological Monographs
Vol. 79, No. 2

Untangling the interactions between climate, vegetation,
and fire in the Deciduous Woodland Zone remains an
important goal of future research and requires more
precise paleoclimate records from this region.
Forest–tundra Zone.—Fire return intervals decreased
slightly, but not significantly, with the establishment of P.
glauca in the mid-Holocene (FRImean 227 yr [170–287];
Fig. 7). Summer temperatures cooled and relative
moisture increased in this zone (but remained drier than
present; Abbott et al. 2000, Anderson et al. 2001,
Edwards et al. 2001). Although temperature and
moisture trends would have reduced fire activity com-
pared to the previous period, the unchanging FRImean
values suggest that these climatic effects were balanced by
the increase in landscape flammability resulting from the
replacement of Populus by P. glauca. In addition,
increased CHARs and peak magnitudes in this zone
(Fig. 4, Appendix B) suggest greater biomass burning per
fire due to increased fuel loads. Unlike other zones, fire
regimes varied across the study region, with significantly
higher fire activity in the east (WK) compared to the west
(RP; Fig. 6). As pollen records do not indicate a gradient
in vegetation that would account for this pattern
(Appendix B), the shorter FRIs at WK suggest a gradient
in climatic controls of fire during this period. Unfortu-
nately, evaluating this possibility is difficult with existing
paleoclimate records. Overall, FRImean values in this zone
are at the lower end of estimated fire rotation periods in
modern forest–tundra (180–1000þ yr; Payette et al. 1989,
Kasischke et al. 2002), possibly reflecting the generally
warmer, drier conditions during the Forest–tundra Zone
compared to modern (Anderson et al. 2004).
Boreal Forest Zone.—A decrease in FRImean at two of
the three study sites coincided with the development of
P. mariana-dominated forests ca. 5500 yr BP (pooled
FRImean of 145 yr [130–163]; Table 2, Figs. 6 and 7). The
absence of a decrease in FRIs at WK is attributable to
the significantly lower FRIs in the previous zone at this
site (as described in the previous paragraph). Few
studies provide detailed information on mid- and late-
Holocene climate change in Alaska (e.g., Hu et al. 2003),
making it impossible to pinpoint climatic factors causing
the fire-regime shift at the transition to P. mariana-
dominated forests. However, several lines of evidence
indicate that effective moisture increased to near-
modern levels by ca. 5000 yr BP and that temperatures
continued to cool into the late Holocene (5000–0 BP;
Ellis and Calkin 1984, Evison et al. 1998, Abbott et al.
2000, Anderson et al. 2001, 2004). Since these changes
should have reduced fire ignition and spread, the
increase in fire activity was likely due to increased
landscape flammability associated with greater conifer
density and the flammable fuels of P. mariana (Viereck
et al. 1986, Johnson 1992). Increased fuel abundance
and greater charcoal production per fire is also
consistent with maximum peak magnitudes and/or
CHARs reached at all sites within this zone (Fig. 4,
Appendix B). Our interpretation that the shift to P.
mariana dominance increased fire occurrence in this
zone is consistent with several other Holocene fire-
history studies from boreal Alaska (Dune, Low, Moose,
and Chokosna Lakes, Fig. 1; Lynch et al. 2002, 2004b,
Hu et al. 2006) and with modeling studies showing
higher fire frequencies with increased P. mariana
abundance (Rupp et al. 2002). However, these findings
contrast with the Paradox Lake record (Anderson et al.
2006), which indicates that FRImean increased from 81
(641 SD) to 130 (666 SD) yr at the time of P. mariana
arrival ca. 4600 yr BP. Thus, although the FRIs for the
boreal forest period are similar to the central Brooks
Range, the direction of change differed. It is possible
that the low density of P. mariana at Parodox Lake
(Anderson et al. 2006) did not cause a large enough
increase in landscape flammability to override the effects
of cooler, moister conditions on fire regimes.
Vegetation mediates the impacts of climate change
on fire regimes
Vegetation influences fire regimes by affecting the size,
abundance, and spatial patterns of fuels across a
landscape, all of which potentially dampen or amplify
the direct impact of climate change on fire regimes. A
unique value of paleorecords is their ability to ‘‘observe’’
shifts in fire regimes as vegetation and climate change
simultaneously. From this perspective, paleorecords
from the south-central Brooks Range support inferences
of modern studies that vegetation can substantially alter
the direct effects of climate change on fire regimes.
Vegetation change amplifies the impact of climate on
fire regimes when climate change directly promotes fire
occurrence (e.g., via increased ignitions and fuel drying)
and increases the abundance and continuity of fuels. For
example, the shift from herb- to birch-dominated shrub
tundra ca. 13 300–14 300 yr BP amplified the direct
effects of climate warming by increasing the abundance
and connectivity of woody fuels. Similarly, as climate
warmed through the late-glacial and early Holocene
periods across the northwestern United States, an
increase in abundance of woody biomass appears to
have amplified climate-driven increases in fire frequen-
cies (Marlon et al. 2006). Modern studies demonstrate
the impact of vegetation on fire regimes at shorter time
scales. For example, interannual climate variability
associated with the El Nin˜o Southern Oscillation
promotes fires in ponderosa pine (Pinus ponderosa P.
& C. Lawson) forests of the southern Rocky Mountains
(Veblen et al. 2000) and southwestern United States
(Baisan and Swetnam 1990) by increasing herbaceous
plant growth in wet springs and then facilitating fuel
drying during summer droughts in following years.
A more unexpected outcome of vegetation change
occurs when vegetation alters the probability of fire in the
opposite direction of climate. Alaskan paleorecords
include two examples of this effect. First, fire activity
decreased during the early Holocene when deciduous
woodlands expanded into shrub tundra, despite warmer-
May 2009 215BROOKS RANGE FIRE HISTORY

than-present summers (Fig. 7). In this case, the low
flammability of deciduous trees reduced fire ignition
and/or spread and overrode the impacts of increased
temperatures on fire occurrence. Second, cooler and
wetter climate in the mid-to-late Holocene would have
reduced the probability of fire at the time climate-induced
increases in P. mariana densities increased landscape
flammability. The net result of these vegetation and
climate shifts was greater fire occurrence, with the positive
effect of increased fuel abundance overriding the negative
effects of climate change (Figs. 6 and 7; Lynch et al. 2002,
Hu et al. 2006). The potential for vegetation change to
negate the direct impacts of climate on fire regimes is not
unique to boreal regions. In grassland ecosystems of
North America, the impact of drought on fire regimes has
been mediated by vegetation change for thousands of
years (Clark et al. 2002, Brown et al. 2005). By reducing
grass cover, centennial-scale droughts limited this fuel
source and reduced fire frequency. Periods of increased
moisture promoted extensive grass cover, which in turn
facilitated frequent fires.
Numerous modern examples illustrate that vegetation
change can alter fire regimes independent of climate. For
example, by reducing the abundance of fine fuels,
livestock grazing can lower fire frequencies and fire
intensity in ecosystems ranging from conifer forests
(Swetnam et al. 1999) to savannas (Roques et al. 2001).
Similarly, exotic grass introductions in a variety of
ecosystems worldwide show that fire frequencies can
increase rapidly when vegetation change adds fuels to a
landscape (e.g., Dantonio and Vitousek 1992). If
vegetation shifts represent a change to or from a fuels-
limited system, their impacts on fire regimes can be
dramatic. Thus, when vegetation shifts change the
degree to which fuels limit fire in an ecosystem, the
impacts of vegetation change can be more important
than the direct cause of the vegetation change itself (e.g.,
land use, exotic species introduction, climate change).
A major conclusion of our study is that, over the past
15 000 years, vegetation shifts in the south-central
Brooks Range strongly mediated the direct impacts of
climate change on fire regimes by modifying the degree
to which fire regimes were fuels limited. Combined with
modern and paleostudies from other regions, these
Alaskan examples lead to the general inference that
future fire regimes will be determined by direct climate–
fire relationships, in addition to the indirect impacts of
climate on vegetation communities. In modern or future
systems where fire occurrence is limited more by the
abundance and continuity of fuels than by climate (i.e.,
ignition and short-term drought), vegetation shifts may
play the more important role in shaping fire regimes
than the direct impacts of climate change alone.
Implications for global change
in arctic and subarctic ecosystems
Fire regimes in past herb tundra, shrub tundra, and
deciduous woodlands reflect the effects of climates and
vegetation biomes that do not have counterparts on the
modern landscape (i.e., no analog climate and vegeta-
tion; Anderson et al. 1989, Bartlein et al. 1991, Williams
and Jackson 2007; Appendix B). Given the potential for
novel vegetation and climate in the future (Edwards et
al. 2005, Williams and Jackson 2007), our results have
important implications for anticipating future fire
regimes in arctic and subarctic ecosystems.
Our finding that vegetation mediated the impacts of
climate change emphasizes the importance of biological–
physical feedbacks in past arctic and subarctic ecosys-
tems. While direct climate–fire relationships may predict
future fire regimes at annual to decadal time scales
(Kasischke et al. 2002, Duffy et al. 2005), the
paleorecord highlights that feedbacks between climate,
vegetation, and fire can override the direct effects of
climate change at longer time scales. For example, in
boreal forests future warming is expected to increase the
area burned in many regions, with a secondary effect of
replacing coniferous with deciduous forest types (Rupp
et al. 2000, Calef et al. 2005, Flannigan et al. 2005,
Johnstone and Chapin 2006). Our finding of infrequent
fires during the warmer-than-present Deciduous Wood-
land Zone implies that future increases in burning could
lower the probability of subsequent fires by favoring
succesional forests with less flammable fuels. If decidu-
ous stands are maintained across the landscape via gap–
phase replacement (Cumming et al. 2000, Johnstone and
Chapin 2006), this negative feedback mechanism could
result in fires being less frequent than would be predicted
by climate’s direct effect on area burned.
The importance of biological–physical feedbacks is also
highlighted by high fire frequencies in past shrub tundra.
Our records provide a clear precedence that shrub-
dominated tundra can sustain higher fire frequencies than
present-day tundra. Thus, the future expansion of tundra
shrubs (Tape et al. 2006, Walker et al. 2006) coupled with
decreased effective moisture (ACIA 2004) could enhance
circumarctic burning and initiate important feedbacks
with the climate system. Recent studies of modern tundra
fires suggest the possibility for both short- and long-term
impacts of increased tundra burning ranging from
increased summer soil temperatures and moisture levels
(Liljedahl et al. 2007) to the release ancient soil carbon
from increased permafrost thawing and organic-layer
consumption (Racine et al. 2006, Liljedahl et al. 2007).
Given the concern over the fate of terrestrial carbon in
tundra and other high-latitude ecosystems (Zimov et al.
1999, Chapin et al. 2000, Mack et al. 2004, Weintraub and
Schimel 2005), the evidence of fires in early Holocene
tundra should motivate research into the controls of
tundra fire regimes and links between tundra burning and
the climate system.
ACKNOWLEDGMENTS
This research was supported by grants from the National
Science Foundation’s Arctic System Science program (to L. B.
Brubaker, P. M. Anderson, and T. A. Brown) and an NSF
Graduate Research Fellowship (to P. E. Higuera). Sampling was
PHILIP E. HIGUERA ET AL.216 Ecological Monographs
Vol. 79, No. 2

conducted under permit from Gates of the Arctic National Park
and the Bureau of Land Management. We thank Ben Clegg,
John Mauro, and Kate Shick for field assistance, Claire Adam,
Ethan Cudaback, Jennifer Leach, Amy Lilienthal (Yambor),
Jason Smith, and Emily Spaulding for laboratory assistance,
and Jim Agee, Dan Gavin, Doug Sprugel, Christopher
Carcaillet, and an anonymous reviewer for constructive
comments on the manuscript.
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APPENDIX A
Supplementary methods: (1) modern analog analysis; (2) Picea pollen grain classification via discriminant analysis; (3) charcoal
analysis: locally defined thresholds, minimum-peak screening, and signal-to-noise index; and (4) likelihood-ratio test for comparing
fire-return-interval distributions (Ecological Archives M079-007-A1).
APPENDIX B
Supplementary results: (1) radiocarbon dates; (2) pollen and charcoal records; (3) distribution of raw charcoal accumulation
rates; (4) Picea pollen grain classification via discriminant analysis; and (5) likelihood-ratio test results for comparisons of pooled
fire-return-interval distributions (Ecological Archives M079-007-A2).
SUPPLEMENT
Matlab source code for statistically comparing distributions of fire return intervals using maximum-likelihood estimates of
Weibull models and a likelihood-ratio test (Ecological Archives M079-007-S1).
May 2009 219BROOKS RANGE FIRE HISTORY