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Article << Previous     |     Next >>   Contents Vol 19(8)

Peak detection in sediment–charcoal records: impacts of alternative data analysis methods on fire-history interpretations

Philip E. Higuera A E, Daniel G. Gavin B, Patrick J. Bartlein B and Douglas J. Hallett C D

A Department of Forest Ecology and Biogeosciences, University of Idaho, Box 83844-1133, Moscow, ID 83844, USA.
B Department of Geography, University of Oregon, Eugene, OR 97493, USA.
C Biogeoscience Institute, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
D School of Environmental Studies, Queen’s University, BioSciences Complex, 3134, Kingston, ON, K7L 3N6, Canada.
E Corresponding author. Email: phiguera@uidaho.edu

International Journal of Wildland Fire 19(8) 996-1014 http://dx.doi.org/10.1071/WF09134
Submitted: 18 November 2009  Accepted: 19 March 2010   Published: 10 December 2010


 
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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 decomposition models (four detrending methods used with two threshold-determination methods) 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.


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