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Assessing reproducibility in sedimentary macroscopic charcoal count data

Published online by Cambridge University Press:  23 September 2022

Lysanna Anderson Open the ORCID record for Lysanna Anderson [Opens in a new window] ,
Liubov Presnetsova ,
David B. Wahl ,
Geoffrey Phelps  and
Alan Gous
Lysanna Anderson*
Affiliation:
U.S. Geological Survey, 345 Middlefield Rd, Menlo Park, CA 94025
Liubov Presnetsova
Affiliation:
U.S. Geological Survey, 345 Middlefield Rd, Menlo Park, CA 94025
David B. Wahl
Affiliation:
U.S. Geological Survey, 345 Middlefield Rd, Menlo Park, CA 94025 Department of Geography, University of California, Berkeley, CA 94720
Geoffrey Phelps
Affiliation:
U.S. Geological Survey, 345 Middlefield Rd, Menlo Park, CA 94025
Alan Gous
Affiliation:
Institute for Computational and Mathematical Engineering, Stanford University, 475 Via Ortega, Stanford, CA 94305
*
*Corresponding author email address: landerson@usgs.gov
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Abstract

Current understanding of global late Quaternary fire history is largely drawn from sedimentary charcoal data. Since publication, CharAnalysis increasingly has been relied upon as a robust method for analyzing these data. However, several underlying assumptions of the algorithm have not been tested. This study uses replicated charcoal count data to examine the assumption of Poisson distribution and reproducibility of peak detection. Results show <10% of the replicate counts are Poisson distributed, a maximum peak replication rate of 60%, and, for >90% of the data, intra-level count differences were larger than the threshold used to identify significance in inter-level differences. A pronounced “edge effect” was observed at the beginning and end of the records, cautioning against validation of results based on sections corresponding to the historical period. The proximal cause for low reproducibility is likely a lack of spatial randomness of charcoal particles at the scale of a core diameter. Until and unless decomposition methods can be developed that accommodate the observed limitations inherent in particle count data, best practices for interpreting charcoal records may be to rely on qualitative interpretations based on smoothed influx values and minimum particle count values in the hundreds.

Type
Research Article
Information
Quaternary Research , Volume 111 , January 2023 , pp. 177 - 196
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Copyright
Copyright © University of Washington. Published by Cambridge University Press, 2022

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