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Pacman profiling: a simple procedure to identify stratigraphic outliers in high-density deep-sea microfossil data

Published online by Cambridge University Press:  08 April 2016

David Lazarus ,
Manuel Weinkauf  and
Patrick Diver
David Lazarus
Affiliation:
Museum für Naturkunde, Invalidenstrasse 43, 10115 Berlin, Germany. E-mail: [email protected]
Manuel Weinkauf
Affiliation:
Freie Universität, Malteserstrasse 74-100, 12249 Berlin, Germany
Patrick Diver
Affiliation:
DivDat Consulting, 1392 Madison 6200, Wesley, Arizona 72773, U.S.A.
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Abstract

The deep-sea microfossil record is characterized by an extraordinarily high density and abundance of fossil specimens, and by a very high degree of spatial and temporal continuity of sedimentation. This record provides a unique opportunity to study evolution at the species level for entire clades of organisms. Compilations of deep-sea microfossil species occurrences are, however, affected by reworking of material, age model errors, and taxonomic uncertainties, all of which combine to displace a small fraction of the recorded occurrence data both forward and backwards in time, extending total stratigraphic ranges for taxa. These data outliers introduce substantial errors into both biostratigraphic and evolutionary analyses of species occurrences over time. We propose a simple method—Pacman—to identify and remove outliers from such data, and to identify problematic samples or sections from which the outlier data have derived. The method consists of, for a large group of species, compiling species occurrences by time and marking as outliers calibrated fractions of the youngest and oldest occurrence data for each species. A subset of biostratigraphic marker species whose ranges have been previously documented is used to calibrate the fraction of occurrences to mark as outliers. These outlier occurrences are compiled for samples, and profiles of outlier frequency are made from the sections used to compile the data; the profiles can then identify samples and sections with problematic data caused, for example, by taxonomic errors, incorrect age models, or reworking of sediment. These samples/sections can then be targeted for re-study.

Type
Articles
Information
Paleobiology , Volume 38 , Issue 1 , Winter 2012 , pp. 144 - 161
Copyright
Copyright © The Paleontological Society

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