Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-19T05:52:52.344Z Has data issue: false hasContentIssue false
  • English
  • Français

Brute-Force Biochronology: Sequencing Paleobiologic First- and Last-Appearance Events by Trial-and-Error

Published online by Cambridge University Press:  21 July 2017

Peter M. Sadler
Peter M. Sadler*
Affiliation:
Department of Earth Science, University of California, Riverside, CA 92521
Get access

Abstract

Computers excel at applying simple, logical rules to prodigious amounts of information. Such is the nature of biochronology. Range charts of first- and last-occurrences of fossil species must be combined from many locations to compensate for local incompleteness of the fossil record. Enlarging the geographic scope adds the complications of faunal migration and provinciality – for which the remedy is yet more information. Expert biostratigraphers have managed to divide Phanerozoic time into hundreds of biozones by limiting the amount of information they consider. A set of biozones specifies the sequence of only a fraction of available species, typically from a single clade in a particular province across a limited time interval. Once the human expertise applied to this task is rendered into logical algorithms, computers can extend the exercise to huge data sets of otherwise unmanageable scope.

Two factors make this computer-assisted sequencing of first- and last-appearance events easy to understand and implement: the biostratigraphers' ground rules are straightforward and the computations, although tediously repetitive, proceed by simply analogy rather than esoteric mathematics. Two other factors force the outcome to be a set of time lines that fit the field data equally well: there is rarely enough information to identify a unique best-fit solution and there is more than one set of expert ground-rules for measuring the fitness of paleobiologic time-lines. A set of equally well-fit time lines serves as an appropriate statement of uncertainty in the order of events. Mapping the local ranges back into a best-fit composite range can reveal biogeographic and taxonomic complications; quality control and interpretation advance together.

Type
Stratigraphic Data
Information
The Paleontological Society Papers , Volume 16: Quantitative Methods in Paleobiology , October 2010 , pp. 271 - 289
Copyright
Copyright © 2010 by the Paleontological Society 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agterberg, F. P. 1990. Automated stratigraphic correlation. Developments in Palaeontology and Stratigraphy, 13:1–424.Google Scholar
Agterberg, F. P., and Gradstein, F. M. 1999. The RASC method for ranking and scaling of biostratigraphic events. Earth Science Reviews, 46:1–25.CrossRefGoogle Scholar
Agterberg, F. P., and Nel, L. D. 1982. Algorithms for the ranking of stratigraphic events. Computers and Geosciences, 8:69–90.CrossRefGoogle Scholar
Alroy, J. 1992. Conjunction among taxonomic distributions and the Miocene mammalian biochronology of the Great Plains, Paleobiology, 18:326–43.CrossRefGoogle Scholar
Alroy, J. 1994. Appearance event ordination: a new biochronological method. Paleobiology, 20:191–207.CrossRefGoogle Scholar
Blackham, M. 1998. The unitary association method of relative dating and its application to archaeological data. Journal of Archaeological Method and Theory, 5:165–207.CrossRefGoogle Scholar
Carney, J. L. and Pierce, R. W. 1995. Graphic Correlation and composite standard databases as tools for the exploration biostratigraphers, p. 23–43. In Mann, K. O., and Lane, H. R. (eds.), Graphic Correlation. Special Publications of the Society of Economic Paleontologists and Mineralogists, 53.CrossRefGoogle Scholar
Cody, R. D., Levy, R. H., Harwood, D. M., and Sadler, P. M. 2008. Thinking outside the zone: high resolution quantitative diatom biochronology for the Antarctic Neogene. Palaeogeography, Palaeoeclimatology, Palaeoecology, 269(1–2):92–121.CrossRefGoogle Scholar
Cooper, R. A., Crampton, J. S., Raine, J. I., Gradstein, F. M., and Morgans, H. E. G., et al. 2001. Quantitative biostratigraphy of the Taranaki Basin, New Zealand: a deterministic and probabilistic approach. American Association of Petroleum Geologists Bulletin, 85:1469–98.Google Scholar
Cuvier, G., and Brongniart, A. 1808. Essai sur la géographie minéralogique des environs de Paris. Annales du Musée Histoire Naturelle de Paris, 11:293–326.Google Scholar
Darwin, C. 1859. The Origin of Species. Murray, London, 490 p.Google Scholar
Dell, R. F., Kemple, W. G., and Tovey, C. A. 1992. Heuristically solving the stratigraphic correlation problem. Proceedings of the 1st. Industrial Engineering Research Conference, 1:293–97.Google Scholar
D'Orbigny, A. 1851. Cours Elementaires de Paleontologie et de Geologie Stratigraphiques. Masson, Paris, 382 p.Google Scholar
Edwards, L. E. 1978. Range charts and no-space graphs. Computers and Geosciences, 4:247–255.CrossRefGoogle Scholar
Elles, G. L., and Wood, E. M. R. 1907. A monograph of British graptolites. Palaeontographical Society Monograph, 61:1–216.Google Scholar
Glover, F. 1989. Tabu search – Part I. Operations Research Society' of America Journal on Computing, 1:190–206.Google Scholar
Gradstein, F. M., and Agterberg, F. P. 1998. Uncertainty in stratigraphic correlation, p. 9–29. In Gradstein, F. M. and Sandvik, K. O. (eds.), Sequence Stratigraphy: concepts and applications, Norwegian Petroleum Society Special Publication 8.Google Scholar
Guex, J. 1977. Une nouvelle méthode d'analyse biochronologique. Bulletin Laboratoire Géologique Lausanne, 224:309–22.Google Scholar
Guex, J. 1991. Biochronological Correlations, Springer Verlag, 252 p.CrossRefGoogle Scholar
Hammer, O., and Harper, D. A. T. 2005. Paleontological Data Analysis. Blackwell, 368 p.CrossRefGoogle Scholar
Hancock, J. M. 1977. The historic development of concepts of biostratigraphic correlation, p. 3–22. In Kauffman, E. G., and Hazel, J. E., (eds.), Concepts and Methods of Biostratigraphy. Dowden, Hutchinson and Ross, Stroudsburg, Pennsylvania.Google Scholar
Hay, W. W., and Southam, J. R. 1978. Quantifying Biostratigraphic Correlation. Annual Reviews of Earth and Planetary Science, 6:353–375.CrossRefGoogle Scholar
Hood, K. C. 1986. GRAPHCOR - Interactive graphic correlation software, version 2.2: copyright 1986–1995, K. C. Hood.Google Scholar
Hutt, J. E. 1975. The Llandovery graptolites of the English Lake District. Palaeontographical Society Monographs, 1–2:1–137.CrossRefGoogle Scholar
Ingber, L. 1993. Simulated annealing: practice versus theory. Mathematical and Computer Modelling, 11:29–57.CrossRefGoogle Scholar
Kemple, W. G., Sadler, P. M., and Strauss, D. J. 1995. Extending graphic correlation to many dimensions: stratigraphic correlation as constrained optimization, p. 65–82. In Mann, K. O., and Lane, H. R. (eds.) Graphic Correlation. Special Publications of the Society of Economic Paleontologists and Mineralogists, 53.CrossRefGoogle Scholar
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. 1983. Optimization by simulated annealing. Science, 220:671–680.CrossRefGoogle ScholarPubMed
Lapworth, C. 1876. On Scottish Monograptidae. Geological Magazine, 2(3):308–321.CrossRefGoogle Scholar
Laudan, R., 1976. William Smith. Stratigraphy without Paleontology. Centaurus, 20(3):210–226.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(2):224–252.CrossRefGoogle Scholar
Macleod, N., and Sadler, P. M. 1995. Estimating the line of correlation, p. 51–64. In Mann, K. O. and Lane, H. R., (eds.) Graphic Correlation. Special Publications of the Society of Economic Paleontologists and Mineralogists, 53.CrossRefGoogle Scholar
McGowran, B., 2005 Biostratigraphy: Microfossils and Geologic Time. Cambridge University Press, 459 p.CrossRefGoogle Scholar
McLaren, D. J. 1988. Detection and significance of mass killings. Canadian Society of Petroleum Geologists Memoir, 14:1–7.Google Scholar
Oppel, A. 1856–58. Die Jura Formation Englands, Frankreichs, und des südwestlichen Deutschlands; nach ihren einzelnen Gliedern eingeteilt und verglichen. Stuttgart, Ebner und Seubert, 875p. (Württembergische Naturwissenschafliche Jahreshefte, 12:121–556, 13:141–396, 14:128–291).Google Scholar
Phillips, J. 1829. Illustrations of the Geology of Yorkshire. T. Wilson, York. 192p.Google Scholar
Pribyl, A. 1940. Die Graptolithen fauna des mittleren Ludlows von Böhmen (oberes esz). Venstník Státního Géologického Ustavu Cceskoslovenské, 16:63–73.Google Scholar
Sadler, P. M. 1981. Sediment accumulation rates and the completeness of stratigraphic sections. Journal of Geology, 89:569–584.CrossRefGoogle Scholar
Sadler, P. M. 2004. Quantitative Biostratigraphy - achieving finer resolution in Global Correlation. Annual Reviews of Earth and Planetary Sciences, v. 32, p. 187–213.CrossRefGoogle Scholar
Sadler, P. M. 2006. Composite time-lines: a means to leverage resolving power from radioisotopic dates and biostratigraphy, p. 145–170. In Olszewski, T. D. (ed.) Geochronology: Emerging Opportunities, Paleontological Society Papers, 12:145–170.Google Scholar
Sadler, P. M. 2009, False coincidences–a pervasive characteristic of the unevenly under-sampled fossil record? 9th North American Paleontological Convention Abstracts, Cincinnati Museum Center Scientific Contributions, 3:256–257.Google Scholar
Sadler, P. M. 2010. Constrained optimization approaches to the paleobiologic correlation and sedation problems: a users guide and reference manual to the Conop family of programs, version 7.61, copyright 1998–2010, P. M. Sadler.Google Scholar
Sadler, P. M., and Cooper, R. A. 2003. Best-fit intervals and consensus sequences: comparison of the resolving power of traditional biostratigraphy and computer-assisted correlation. p. 49–94. In Harries, P. (ed.) High Resolution Stratigraphic Approaches in Paleontology. Kluwer-Academic Press.Google Scholar
Sadler, P. M., Cooper, R. A., and Melchin, M. 2009. High-resolution, early Paleozoic (Ordovician-Silurian) time scales. Geological Society of America Bulletin, 121:887–906.CrossRefGoogle Scholar
Sadler, P. M., and Sabado, J. A. 2009. Automated correlation, seriation, and the treatment of biotic dissimilarity. Museum of Northern Arizona Bulletin 65:21–35.Google Scholar
Savary, J., and Guex, J. 1991. Biograph: un nouveau programme de construction des correlations biochronologique basées sur les associations unitaires. Bulletin Laboratoire Géologique Université Lausanne 313:317–40.Google Scholar
Savary, J., and Guex, J. 1999. Discrete biochronological scales and unitary association: description of the BioGraph computer program. Mémoire Géologique Lausanne, 34:1–281.Google Scholar
Sedgwick, A., and Murchison, R. I. 1839. Classification of the older stratified rocks of Devonshire and Cornwall. Philosophical Magazine and Journal of Science Series 3, 14:241–260.Google Scholar
Shaw, A. B. 1964. Time in Stratigraphy. New York: McGraw Hill. 365 pp.Google Scholar
Shaw, A. B. 1995. Early history of graphic correlation. p. 15–19. In Mann, K. O., and Lane, H. R. (eds.) Graphic Correlation. Special Publications of the Society of Economic Paleontologists and Mineralogists, 53.CrossRefGoogle Scholar
Smith, W., 1799, Tabular view of the order of strata in the vicinity of Bath with their respective organic remains.Google Scholar
Spencer, H. 1864. The Principles of Biology, Volume 1. Williams and Norgate. London. 492p.Google Scholar
Tipper, J. C. 1988. Techniques for quantitative stratigraphic correlations: a review and annotated bibliography. Geological Magazine, 125:475–94.CrossRefGoogle Scholar
Wood, E. M. R. 1900. The Lower Ludlow formation and its graptolite fauna. Quarterly Journal of the Geological Society of London, 56:415–492.CrossRefGoogle Scholar