Peak detection in sediment–charcoal records:
impacts of alternative data analysis methods
on fire-history interpretations
Philip E. HigueraA,E, Daniel G. GavinB, Patrick J. BartleinB
and Douglas J. HallettC,D
ADepartment of Forest Ecology and Biogeosciences, University of Idaho,
Box 83844-1133, Moscow, ID 83844, USA.
BDepartment of Geography, University of Oregon, Eugene, OR 97493, USA.
CBiogeoscience Institute, University of Calgary, 2500 University Drive NW,
Calgary, AB, T2N 1N4, Canada.
DSchool of Environmental Studies, Queen’s University, BioSciences Complex,
3134, Kingston, ON, K7L 3N6, Canada.
ECorresponding author. Email: phiguera@uidaho.edu
Abstract. Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to
reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and
then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation
of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight
alternative decompositionmodels (four detrendingmethods usedwith two threshold-determinationmethods) and evaluate
their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the
combination of a globally defined threshold with specific detrending methods can produce strongly biased results,
depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated
by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the
alternative decomposition methods on three previously published charcoal records largely supports our conclusions from
simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false
positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are
warranted with a given sediment–charcoal record.
Additional keywords: bias, paleoecology, sensitivity.
Introduction
High-resolution charcoal records are an increasingly common
source of fire-history information, particularly in ecosystems
where tree-ring records are short relative to average fire-return
intervals (Gavin et al. 2007). Over the past several decades,
numerous studies have used peaks in charcoal accumulation in
sediment records to estimate the timing of ‘fire episodes’, one or
more fires within the sampling resolution of the sediment record
(Whitlock and Larsen 2001). Identifying fire episodes from
charcoal records is most promising when fires: (1) are large;
(2) burn with high severity; and (3) recur with average intervals
at least five times the sampling resolution of the sediment record
(Clark 1988b; Whitlock and Larsen 2001; Higuera et al. 2005,
2007). Sediment–charcoal records are thus particularly valuable
for studying stand-replacing fire regimes in boreal and subalpine
forests, where all three of these conditions are typically met.
Interpreting fire episodes from sediment–charcoal records
would be straightforward if they were characterised by low
levels of charcoal punctuated by unambiguous peaks. In reality,
however, charcoal records are complex and non-stationary,
i.e. their mean and variance change over time (Clark et al.
1996; Clark and Patterson 1997; Long et al. 1998). Empirical
and theoretical studies (e.g. Marlon et al. 2006; Higuera et al.
2007) suggest that non-stationarity in charcoal records can arise
from at least two sets of processes: (1) changes in the fire regime,
including the rate of burning, the intensity of fires, the type of
vegetation burned, and thus charcoal production per unit time;
or (2) changes in the efficiency of charcoal delivery to the lake
centre (taphonomy) due to changing rates of slope wash or
within-lake redeposition. The latter process, known as sediment
focussing, can greatly affect the sediment accumulation rate as a
lake fills in over time (Davis et al. 1984; Giesecke and Fontana
2008) and may produce long-term trends in charcoal records
unrelated to changes in the fire regime. Recognising the impor-
tance of these processes, paleoecologists have applied a range of
statistical methods to charcoal data in order to isolate the signal
related to ‘local’ fire occurrence (e.g. within 0.5–1.0 km; Gavin
et al. 2003; Lynch et al. 2004a; Higuera et al. 2007) and
reconstruct fire history. Despite the proliferation of statistical
methods for peak identification, seemingly no study has
CSIRO PUBLISHING
International Journal of Wildland Fire 2010, 19, 996–1014 www.publish.csiro.au/journals/ijwf

IAWF 2010 10.1071/WF09134 1049-8001/10/080996

discussed the assumptions underlying alternative methods and
their impacts on fire-history interpretations.
Here, we address several key issues related to peak identifica-
tion in high-resolution, macroscopic charcoal recordsA by using
simulated and empirical charcoal records. We start by discussing
some important statistical properties of macroscopic charcoal
records and then describe themotivation for statistical treatments.
We briefly review how different methods have been applied, and
then introduce a typology of methods, including their respective
assumptions and justifications. Second, we illustrate and quantify
the biases that these techniques can introduce to fire-history
interpretations by applying them to simulated charcoal records.
Third, we apply the same methods to three previously published
charcoal records to demonstrate potential biases in empirical
records, and we introduce a technique to minimise some of these
biases. Finally, we conclude with recommendations of specific
methodologies and a discussion of how analysts can evaluate the
suitability of records for peak identification rather than other
qualitative or quantitative analyses.
Temporal variability in charcoal time series
Charcoal time series can be generally characterised as ‘noisy’,
and they contain many forms of non-stationarity, including
changing short-term variability superimposed on a slowly
varying mean (Long et al. 1998; Higuera et al. 2007). Changes
in variability (i.e. heteroscedasticity) have implications for the
particular goal of data analysis. When the goal is to quantify
changes in total charcoal input, as an index of biomass burning
for example, heteroscedasticity violates the assumptions of
parametric statistics useful in this context, e.g. analysis of
variance and regression. In particular, in analysis of variance
(or in the t-test of the difference of means in the case of two
periods), heteroscedasticity increases the probability of Type I
error, falsely inferring significant differences between periods
(Underwood 1997). Similarly, in regression analysis, fitting a
trend line to charcoal data with changing variability over time
can increase the variability of the slope coefficient. Changes
in variability (besides being interesting in their own right) can
thus lead to false conclusions about the significance of long-
term trends or differences between different parts of a record. In
practice, heteroscedasticity is usually dealt with by applying a
‘variance-stabilising transformation’ (Emerson 1983) that acts
to homogenise variance across a record. As will be illustrated
below, when the goal of charcoal analysis is peak identification,
transformation can lead to the exaggeration of some peaks
and suppression of others. Consequently, the specific approach
taken (whether to transform or not) should depend on the overall
focus of an analysis. In this paper, we focus on the goal of
detecting local fires through peak detection.
Analytical methods for inferring local fire occurrence
Following the pioneering work of Clark (1988b, 1990) in which
fire events surrounding small lakes were identified from
charcoal in thin-sections of laminated sediments, similar
approaches were developed for quantifying macroscopic char-
coal abundance and subsequently adopted by a large number of
research groups (Table 1; see also Whitlock and Larsen 2001).
Most techniques quantify charcoal as either the total number
of pieces or surface area (mm2) of charcoal in a particular
size class, within volumetric subsamples taken contiguously
through sediment cores (typically at 0.5- to 1.0-cm resolution,
corresponding to ,10–25-year resolution for most lakes). The
resulting concentration of charcoal (pieces cm
3, ormm
2 cm
3)
in each level is multiplied by the estimated sediment accumula-
tion rate (cmyear
1) to obtain the charcoal accumulation rate
(CHAR, pieces cm
2 year
1 or mm
2 cm
2 year
1). Sediment
accumulation rates, and the age of each sample, are estimated by
an age–depth model based on radiometric dates, tephra layers,
and any additional sources of age information. The use of accu-
mulation rates can potentially correct for changing sediment
accumulation rates that would dilute or concentrate charcoal in a
given volume of sediment, and as mentioned above, may also be
affected by sediment focussing processes. Usually, the CHAR
series is interpolated to a constant temporal resolution to account
for unequal sampling intervals resulting from variable sediment
accumulation rates. This step is necessary to develop threshold
statistics that are not biased to a particular portion of a record, and
to standardise within- and between-site comparisons.B Hereafter,
we refer to the interpolated CHAR series as C. The analytical
choices and sources of error in the development of a charcoal
record are briefly summarised in Table 2 and discussed in detail
by Whitlock and Larsen (2001).
At this point, most C series can be characterised as
irregular time series with discrete peaks superimposed on a
slowly varying mean. Although the size of any individual
peak reflects the size, location, and charcoal production of
individual fires, the average size of peaks may change through
time, contributing to a slowly changing variance. This non-
stationaritymay arise, as discussed above, owing to variations in
charcoal production per unit time or variable taphonomic and
sedimentation processes. Without knowledge of whether non-
stationarity is due to changes in taphonomy and sedimentation
or to real changes in fire history, it is reasonable to stabilise the
variance of peak heights so as to not ‘pass over’ periods of low
charcoal. This motivates the manipulation of C to produce a
stationary series in which all local fires would theoretically
result in a similar range of peak sizes. Doing so would allow for
the application of a single global threshold value to the final
series to separate fire-related from non-fire-related peaks.
In practice, determining the size of peaks that represents local
fires involves a three-step ‘decomposition’ of theC series (Clark
et al. 1996; Long et al. 1998; Fig. 1). First, the slowly varying
mean, or ‘background’ component, Cback, is modelled through a
curve-fitting algorithm, e.g. a locally weighted regression that
is robust to outliers (e.g. Cleveland 1979). The window size for
this smoothing varies between studies but is typically between
100 and 1000 years. Background estimation may be preceded
by transformingC (e.g. logarithmically). Second, thebackground
trend is removed from the series by subtraction (C
Cback) or
AWe refer to macroscopic charcoal records as those quantifying charcoal not passing through a sieve of 125 mm or larger.
BWhen sampling intervals are not standardised within a record or between two records, then biases may be introduced when applying criteria uniformly.
Interpolation helps minimise, but not remove, this bias, as noted in the last section of this paper.
Fire history from sediment charcoal records Int. J. Wildland Fire 997