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On the scaling and standardization of charcoal data in paleofire reconstructions

Cite this dataset

McMichael, Crystal; Heijink, Britte; Bush, Mark; Gosling, William (2021). On the scaling and standardization of charcoal data in paleofire reconstructions [Dataset]. Dryad.


Understanding the biogeography of past and present fire events is particularly important in tropical forest ecosystems, where fire rarely occurs in the absence of human ignition. Open science databases have facilitated comprehensive and synthetic analyses of past fire activity, but charcoal datasets must be standardized (scaled) because of variations in measurement strategy, sediment type, and catchment size.  Here, we: i) assess how commonly used metrics of charcoal scaling perform on datasets from tropical forests; ii) introduce a new method called proportional relative scaling, which down-weights rare and infrequent fire; and iii) compare the approaches using charcoal data from four lakes in the Peruvian Amazon. We found that Z-score transformation and relative scaling (existing methods) distorted the structure of the charcoal peaks within the record, inflating the variation in small-scale peaks and minimizing the effect of large peaks. Proportional relative scaling maintained the structure of the original non-scaled data and contained zero values for the absence of fire. Proportional relative scaling provides an alternative scaling approach when the absence of fire is central to the aims of the research or when charcoal is infrequent and occurs in low abundances.


Charcoal was processed and analyzed from four lake sediments in the Peruvian Amazon. The charcoal data were standardized in three different ways: z-score transformation, relative scaling, and proportional relative scaling. See the methods section of the main article for an extended description of data collection, laboratory processing and analyses, and data standardization techniques.

Usage notes

The dataset contains four data files (one per lake) and a readme file to describe the data files. 


European Research Council, Award: ERC StG 853394