Abstract
Charcoal identification and the quantification of its abundance in sedimentary archives is commonly used to reconstruct fire frequency and the amounts of biomass burning. There are, however, limited metrics to measure past fire temperature and fuel type (i.e. the types of plants that comprise the fuel load), which are important for fully understanding the impact of past fire regimes. Here, we expand the modern reference dataset of charcoal spectra derived from micro-Fourier Transformed Infrared Spectroscopy (FTIR) and apply an analogue matching model to estimate the maximum pyrolysis temperature and the type of plant material burned. We generated laboratory-created reference charcoal from nine plant species that were heated to six temperature categories (100 °C increments between 200 °C–700 °C). The analogue matching approach used on the FTIR spectra of charcoal estimated the maximum pyrolysis temperatures with an accuracy of 57%, which improved to 93% when accuracy was considered ±100 °C. Model accuracy for the type of plant material burned was 38% at the species level, which increased to 67% when species were grouped into trait-based categories. Our results show that analogue matching is an effective approach for estimating pyrolysis temperature and the type of plant material burned, and we suggest that it can also be applied to charcoal found in palaeoecological records, improving our understanding of past fire regimes and fuel dynamics.
Highlights
Modern synergies between direct human activity and the indirect influence of climate change are altering fire regimes through positive feedbacks that increase fire susceptibility, fuel loads, and fire intensity (IPCC, 2014; McLauchlan et al, 2020; Pyne, 2001)
There are, limited metrics to measure past fire temperature and fuel type, which are important for fully understanding the impact of past fire regimes
The analogue matching approach used on the Fourier Transformed Infrared Spectroscopy (FTIR) spectra of charcoal estimated the maximum pyrolysis temperatures with an accuracy of 57%, which improved to 93% when accuracy was considered ±100 ◦C
Summary
Modern synergies between direct human activity and the indirect influence of climate change are altering fire regimes through positive feedbacks that increase fire susceptibility, fuel loads, and fire intensity (IPCC, 2014; McLauchlan et al, 2020; Pyne, 2001). Fire regimes can be defined as the general characteristics of recurrent fires through time (size, extent, frequency, intensity) (Gill, 1975; Keeley, 2009), and the magnitude of the ecological effects of fire (severity) (organic matter loss sensu Keeley, 2009; including impact on vegetation sensu McLauchlan et al, 2020) (Table 1). Changes in fire severity (sensu Keeley, 2009), and frequency (for definitions of fire characteristics, see Table 1) can be inferred from observational data, such as maps of area burned, fire occurrence records, and satellite imagery (Abedi Gheshlaghi et al, 2020; Giglio et al, 2016; Morgan et al, 2014; Roy et al, 2006; Weng, 2005; White et al, 1996).
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