Using tree-ring records to calibrate peak detection in fire reconstructions based on sedimentary charcoal records

We compared fire episodes over the past 150 years reconstructed using charcoal particles retrieved from well-dated sediment deposits from two small lakes in the eastern Canadian boreal forest, with dendrochronological reconstructions of fire events from the corresponding watersheds. Fire scars and age structure of living trees highlighted three fire events (ad 1890, 1941, and 1989). To explore the ability to detect these fire events based on sedimentary charcoal records, we explored the influence of two user-determined parameters of a widely used peak-detection algorithm (the CharAnalysis software): (1) the temporal resolution used to interpolate charcoal series and (2) the width of the smoothing window used to model background noise. The signal-to-noise index (SNI) is often used to evaluate the ability to detect peaks in sedimentary charcoal records, which can be related to fire events. SNI values >3 identify records appropriate for peak detection. Selecting standard settings in paleoecological studies (median temporal resolution of the entire sequence and 500- to 1000-year window width) yielded higher global SNI values but failed to detect most recent fire events. Instead, selecting a shorter reference period (the past ~150 years) to determine the temporal resolution to interpolate the charcoal series and a narrower smoothing window (100 years) best matched the tree-ring data despite lower SNI values (often <3.0). However, Holocene fire history differed markedly when reconstructed using different smoothing window widths (100–150 years vs >300 years). Consequently, we suggest using the smallest window width yielding a SNI >3. Practitioners must not necessarily focus on obtaining the highest possible SNI, usually related to wide smoothing windows. We also suggest that fire history reconstructions should focus on core sections presenting fairly constant sedimentation rates. Alternatively, sediments could be subsampled after age–depth models have been obtained.