Canadian Forest Service Publications

Paleofire reconstruction based on an ensemble-member strategy applied to sedimentary charcoal. 2013. Blarquez, O.; Girardin, M.P.; Leys, B.; Ali, A.A.; Aleman, J.C.; Bergeron, Y.; Carcaillet, C. Geophys. Res. Lett. 40:2667-2672.

Year: 2013

Available from: Laurentian Forestry Centre

Catalog ID: 35078

Language: English

CFS Availability: PDF (request by e-mail)

Abstract

Paleofire events obtained from the statistical treatment of sedimentary charcoal records rely on a number of assumptions and user’s choices, increasing the uncertainty of reconstructions. Among the assumptions made when analyzing charcoal series is the choice of a filtering method for raw Charcoal Accumulation Rate (CHARraw). As there is no ultimate CHARraw filtering method, we propose an ensemble-member approach to reconstruct fire events. We modified the commonly used procedure by including a routine replicating the analysis of a charcoal record using custom smoothing parameters. Dates of robust fire events, uncertainties in fire-return intervals and fire frequencies are derived from members’ distributions. An application of the method is used to quantify uncertainties due to data treatment in two CHARraw sequences from two different biomes, subalpine and boreal.

Plain Language Summary

To reconstruct a region's fire history over several centuries, researchers sample lakebed charcoal sediments. During a fire, smoke particles are deposited in these lake sediments and form layers that vary in thickness based on the intensity of the fire. Researchers then link the results of these stratification analyses with historically prevailing climatic conditions and with local vegetation.

However, there is a certain margin of error in the number and frequency of fires deduced by these charcoal analyses. Some fires may be detected or go undetected, depending on the numerical approach used.

In this study, researchers developed a method to quantify this margin of error in order to take it into account in their research. This approach makes it possible to see whether observed fire trends are the product of analytical bias or a real phenomenon underway that can be attributed to changes in climate or vegetation, for example.

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