2021
DOI: 10.21425/f5fbg49431
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On the scaling and standardization of charcoal data in paleofire reconstructions

Abstract: 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 fo… Show more

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Cited by 13 publications
(9 citation statements)
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“…Without such a protocol a standardization method is necessary to account for differences in laboratory procedure, catchment size, and sediment properties when comparing charcoal amounts across sites (44). We performed proportional relative scaling to standardize the charcoal abundance data from each lake record (45), and report it as the Charcoal Index Value.…”
Section: Analysis Of Lake Sedimentsmentioning
confidence: 99%
See 1 more Smart Citation
“…Without such a protocol a standardization method is necessary to account for differences in laboratory procedure, catchment size, and sediment properties when comparing charcoal amounts across sites (44). We performed proportional relative scaling to standardize the charcoal abundance data from each lake record (45), and report it as the Charcoal Index Value.…”
Section: Analysis Of Lake Sedimentsmentioning
confidence: 99%
“…The scaled measurements are then multiplied by the proportion of samples within the record containing charcoal (f/N). Proportional relative scaling is particularly robust in assessing fire in systems where fire is infrequent or rare, because is used as the true absence value, and samples containing small or infrequent amounts of charcoal are down-weighted (45). The scaled data are reported as the Charcoal Index value.…”
Section: ℎ = ( * 100) * /mentioning
confidence: 99%
“…For charcoal data, we performed proportional relative scaling to standardize the measurements of each lake record from 0 to 100, to account for differences in quantification methods (e.g. particle counts, surface area), laboratory procedure, catchment size and sediment properties [62].…”
Section: Methodsmentioning
confidence: 99%
“…We therefore standardized the charcoal data in two ways. First, we used the approach proposed by McMichael et al (2021), using their “proportional relative scaling” within each site, that is, as (cnormalicmin)/(cmaxcmin), where c i is the charcoal in the sample, c min is the minimum charcoal count in the record, and c max is the maximum charcoal count in the record. This value was then multiplied by the proportion of samples in the record with charcoal.…”
Section: Methodsmentioning
confidence: 99%