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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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References

Literature Cited

Alroy, J., Marshall, C. R., Bambach, R. K., Bezusko, K., Foote, M., Fürsich, F. T., Hansen, T. A., Holland, S. M., Ivany, L. C., Jablonski, D., Jacobs, D. K., Jones, D. C., Kosnik, M. A., Lidgard, S., Low, S., Miller, A. I., Novack-Gottshall, P. M., Olszewski, T. D., Patzkowsky, M. E., Raup, D. M., Roy, K., Sepkoski, J. J., Sommers, M. G., Wagner, P. J., and Webber, A. 2001. Effects of sampling standardization on estimates of Phanerozoic marine diversification. Proceedings of the National Academy of Sciences U.S.A. 98:6261–6266.Google Scholar
Berggren, W. A., Kent, D. V., Flynn, J. J., and Van Couvering, J. A. 1985. Cenozoic geochronology. Geological Society of America Bulletin 96:1407–1418.Google Scholar
Berggren, W. A., Kent, D. V., Swisher, C. C., and Aubry, M-P. 1995. A revised Cenozoic geochronology and chronostratigraphy. Pp. 129–212 in Berggren, W. A., Kent, D. V., Aubry, M.-P., and Hardenbol, J., eds. Geochronology, time scales and stratigraphic correlation: framework for an historical geology. Society of Economic Paleontologists and Mineralogists, Tulsa, Okla.Google Scholar
Bohling, G. 2005. Chronos age-depth plot: a Java application for stratigraphic data analysis. Geosphere 1:78–84.Google Scholar
Bolli, H. M., Saunders, J. B., and Perch-Nielsen, K. 1985. Plankton stratigraphy. Cambridge University Press, Cambridge.Google Scholar
Cody, R. D., Levy, R., Harwood, D. M., and Sadler, P. M. 2008. Thinking outside the zone: high-resolution quantitative diatom biochronology for the Antarctic Neogene. Palaeogeography, Palaeoclimatology, Palaeoecology 260:92–121.Google Scholar
De Wever, P. 1981. Spyrids, artostrobiids, and Cretaceous radiolarians from the western Pacific, Deep Sea Drilling Project Leg 61. Pp. 507–520 in Larson et al. 1981.Google Scholar
Finkel, Z. V., Katz, M. E., Wright, J. D., Schofield, O., and Falkowski, P. G. 2005. Climatically driven macroevolutionary patterns in the size of marine diatoms over the Cenozoic. Proceedings of the National Academy of Sciences U.S.A. 102:8927–8932.Google Scholar
Finkel, Z. V., Sebbo, J., Feist-Burkhardt, S., Irwin, A. J., Katz, M. E., Schofield, O., Young, J., and Falkowski, P. G. 2007. A universal driver of macroevolutionary change in the size of marine phytoplankton over the Cenozoic. Proceedings of the National Academy of Sciences U.S.A. 104:20416–20420.Google Scholar
Foote, M., Crampton, J. S., Beu, A. G., and Cooper, R. A. 2008. On the bidirectional relationship between geographic range and taxonomic duration. Paleobiology 34:421–433.Google Scholar
Gradstein, F. 1985. Quantitative stratigraphy. D. Reidel, Dordrecht.Google Scholar
Johnson, D. A. 1990. Radiolarian biostratigraphy in the central Indian Ocean, Leg 115. In Duncan, R. A., Backman, J., and Peterson, L. C., eds. Proceedings of the Ocean Drilling Program, Scientific Results 115:395–410Ocean Drilling Program, College Station, Tex.Google Scholar
Johnson, D. A.and Nigrini, C. A. 1985. Synchronous and time-transgressive Neogene radiolarian datum levels in the equatorial Indian and Pacific Oceans. Marine Micropaleontology 9:489–523.Google Scholar
Kucera, M., and Schönfeld, J. 2007. The origin of modern oceanic foraminiferal faunas and Neogene climate change. Pp. 409–425 in Williams, M., Haywood, A. M., Gregory, F. J., and Schmidt, D. N., eds. Deep-time perspectives on climate change: marrying the signal from computer models and biological proxies. The Geological Society, London.Google Scholar
Labracherie, M. 1985. Quaternary radiolarians from the Equatorial Pacific, Deep Sea Drilling Project Leg 85. In Mayer, L., Theyer, F., Thomas, E., et al., eds. Initial Reports of the Deep Sea Drilling Project 85:499–509U.S. Government Printing Office, Washington, D.C.Google Scholar
Larson, R. L., et al., eds. 1981. Initial Reports of the Deep Sea Drilling Project 61. U.S. Government Printing Office, Washington, D.C.Google Scholar
Lazarus, D. B. 1992. Age Depth Plot and Age Maker: age depth modeling on the Macintosh series of computers. Geobyte 7:7–13.Google Scholar
Lazarus, D. B. 1994. The Neptune Project: a marine micropaleontology database. Mathematical Geology 26:817–832.Google Scholar
Lazarus, D. B. 2011. The deep-sea microfossil record of macroevolutionary change in plankton and its study. In Smith, A.and McGowan, A., eds. Comparing the geological and fossil records: implications for biodiversity studies. The Geological Society, London.Google Scholar
Lazarus, D. B., Spencer-Cervato, C., Pianka-Biolzi, M., Beckmann, J. P., von Salis, K., Hilbrecht, H., and Thierstein, H. R. 1995. Revised chronology of Neogene DSDP holes from the world ocean. Ocean Drilling Program, Technical Note 24.Google Scholar
Liow, L. H., and Stenseth, N. C. 2007. The rise and fall of species: implications for macroevolutionary and macroecological studies. Proceedings of the Royal Society of London B 274:2745–2752.Google Scholar
Liow, L. H., Skaug, H. J., Ergon, T., and Schweder, T. 2010. Global occurrence trajectories of microfossils: environmental volatility and the rise and fall of individual species. Paleobiology 36:224–252.Google Scholar
Marshall, C. R. 1990. Confidence intervals on stratigraphic ranges. Paleobiology 16:1–10.Google Scholar
Marshall, C. R. 1994. Confidence intervals on stratigraphic ranges: partial relaxation of the assumption of randomly distributed fossil horizons. Paleobiology 20:459–469.Google Scholar
Marshall, C. R. 1997. Confidence intervals on stratigraphic ranges with nonrandom distributions of fossil horizons. Paleobiology 23:165–173.Google Scholar
Moore, T. C. J., Shackleton, N. J., and Pisias, N. G. 1993. Paleoceanography and the diachrony of radiolarian events in the eastern equatorial Pacific. Paleoceanography 8:567–586.Google Scholar
Nigrini, C. 1985. Radiolarian biostratigraphy in the central equatorial Pacific, Deep Sea Drilling Project Leg 85. In Mayer, L., Theyer, F., Thomas, E., et al., eds. Initial Reports of the Deep Sea Drilling Project 85:511–551U.S. Government Printing Office, Washington, D.C.Google Scholar
Nigrini, C., and Sanfilippo, A. 2001. Cenozoic radiolarian stratigraphy for low and middle latitudes. Ocean Drilling Program, College Station, Tex. http://www-odp.tamu.edu/publications/tnotes/tn27/index.html.Google Scholar
Nigrini, C., Sanfilippo, A., and Moore, T. C. Jr. 2006. Cenozoic radiolarian biostratigraphy: a magnetobiostratigraphic chronology of Cenozoic sequences from ODP Sites 1218, 1219, and 1220, Equatorial Pacific. Proceedings of the Ocean Drilling Program, Scientific Results 199:1–76.Google Scholar
Prothero, D., and Lazarus, D. B. 1980. Planktonic microfossils and the recognition of ancestors. Systematic Zoology 29:119–129.Google Scholar
Rabosky, D. L., and Sorhannus, U. 2009. Diversity dynamics of marine planktonic diatoms across the Cenozoic. Nature 247:183–187.Google Scholar
Riedel, W. R., and Sanfilippo, A. 1978. Stratigraphy and evolution of tropical Cenozoic radiolarians. Micropaleontology 23:61–96.Google Scholar
Roberts, D. G., Schnitker, D., et al., eds. 1984. Initial Reports of the Deep Sea Drilling Project 81. U.S. Government Printing Office, Washington, D.C.Google Scholar
Rosner, B. 1983. Percentage points for a generalized ESD Many-Outlier procedure. Technometrics 25:165–172.Google Scholar
Sadler, P. M. 2004. Quantitative biostratigraphy: achieving finer resolution in global correlation. Annual Review of Earth and Planetary Sciences 32:187–213.Google Scholar
Sadler, P. M. 2010. Brute-force biochronology: sequencing paleobiologic first- and last-appearance events by trial and error. In Alroy, J.and Hunt, G., eds. Quantitative methods in paleobiology. Paleontological Society Short Course, 30 October 2010. Paleontological Society Papers 16:271–289.Google Scholar
Sanfilippo, A., Westberg, M. J., and Riedel, W. R. 1981. Cenozoic radiolarians at Site 462, Deep Sea Drilling Project Leg 61, western tropical Pacific. Pp. 495–505 in Larson et al. 1981.Google Scholar
Sanfilippo, A., Westberg, M. J., and Riedel, W. R. 1985. Cenozoic radiolaria. Pp. 631–712 in Bolli et al. 1985.Google Scholar
Sepkoski, J. J. Jr. 1978. A kinetic model of Phanerozoic taxonomic diversity I. analysis of marine orders. Paleobiology 4:223–251.Google Scholar
Shaw, A. B. 1964. Time in stratigraphy. McGraw-Hill, New York.Google Scholar
Spencer-Cervato, C. 1999. The Cenozoic deep sea microfossil record: explorations of the DSDP/ODP sample set using the Neptune database. Palaeontologica Electronica 2, http://www.nhm.ac.uk/hosted_sites/pe/1999_2/toc.htm.Google Scholar
Spencer-Cervato, C., Lazarus, D. B., Beckmann, J. P., Perch-Nielsen, K. von S., and Biolzi, M. 1993. New calibration of Neogene radiolarian events in the North Pacific. Marine Micropaleontology 21:261–294.Google Scholar
Spencer-Cervato, C., Thierstein, H. R., Lazarus, D. B., and Beckmann, J. P. 1994. How synchronous are Neogene marine plankton events? Paleoceanography 9:739–763.Google Scholar