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Statistical pattern recognition in paleontology using SIMCA-MACUP

Published online by Cambridge University Press:  14 July 2015

Kuo-Yen Wei
Kuo-Yen Wei*
Affiliation:
Department of Geology and Geophysics, Yale University, P.O. Box 6666, New Haven, Connecticut 06511-8130
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Abstract

This paper describes a pattern recognition method, Soft Independent Modeling of Class Analogy-Modeling And Classification Using Partial least squares (SIMCA-MACUP), and demonstrates its application to two common paleontological problems—identification and prediction. SIMCA-MACUP performs statistical pattern recognition at four hierarchical levels. At level 1, SIMCA builds disjoint principal component models to characterize multivariate data patterns of several classes. At level 2, new, unknown objects are either classified into one of the classes, or recognized as outliers. At levels 3 and 4, the MACUP method models the relationship between external (dependent) variables and internal (independent) variables, and allows prediction of the values of external variables of new objects which are classified at level 2.

The applicability of the SIMCA-MACUP has been demonstrated in a case study of the Pliocene planktic foraminiferal clade Globoconella. Two morphotypes of the clade Globorotalia (Globoconella) puncticulata and Globorotalia (Globoconella) inflata were characterized in a reference sample with SIMCA. Ancestral forms of the two morphotypes were traced through the early phylogenetic history of the clade, resulting in a reconstruction of the branching pattern of the divergence of G. inflata from G. puncticulata. The apertural height and apertural width of specimens in the ancestral stocks were then treated as if there were two “external” variables, and predicted using MACUP. The predicted values show slight discrepancy from the observed values. The deviation is size-dependent, indicating a decoupling of the apertural shape from the size of foraminifers during the branching process.

Type
Research Article
Information
Journal of Paleontology , Volume 68 , Issue 4 , July 1994 , pp. 689 - 703
Copyright
Copyright © The Paleontological Society 

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References

Albano, C., Bolmqvist, G., Coomans, D., Dunn III, W. J., Edlund, U., Eliasson, B., Hellberg, S., Johansson, E., Johnels, D., Norden, B., Sjöström, M., Söderström, B., Wold, H., and Wold, S. 1981. Pattern recognition by means of disjoint principal component models (SIMCA): philosophy and methods, p. 183–218. In Hoskuldson, A. and Esbensen, K. (eds.), Proceedings Symposium i anvent statistik, Neucc, Recau and Recku, Kopenhamn. Danish Society of Theoretical Statistics, Copenhagen.Google Scholar
Albano, C., Dunn III, W., Edlund, U., Johansson, E., Norden, B., Sjöström, M., and Wold, S. 1978. Four levels of pattern recognition. Analytica Chimica Acta, 103:429–443.Google Scholar
Aminzadeh, F. 1987. Pattern recognition & image processing. In Helbig, K. and Treitel, S. (eds.), Handbook of Geophysical Exploration, Section 1, Seismic Exploration. Geophysical Press, London-Amsterdam, 568 p.Google Scholar
Bayer, U. 1985. Pattern recognition problems in geology and paleontology. In Friedman, G. M. and Seilacher, A. (eds.), Lecture Notes in Earth Sciences. Springer-Verlag, Berlin, 229 p.Google Scholar
Brown, S. D. 1990. Chemometrics. Analytical Chemistry, 62:84R–101R.CrossRefGoogle ScholarPubMed
Brown, S. D., Barker, T. Q., Larivee, R. J., Monfre, S. L., and Wilk, H. R. 1988. Chemometrics. Analytical Chemistry, 60:252R–273R.Google Scholar
Gonzales, R. C. 1987. An overview of image processing and pattern recognition techniques, p. 45–78. In Aminzadeh, F. (ed.), Pattern Recognition & Image Processing. Geophysical Press, London-Amsterdam.Google Scholar
Hageman, S. J. 1992. Alternative methods for dealing with nonnormality and heteroscedasticity in paleontological data. Journal of Paleontology, 66:857–867.CrossRefGoogle Scholar
Haugen, J.-E., Sejrup, H.-P., and Vogt, N. B. 1989. Chemotaxonomy of Quaternary benthic foraminifera using amino acids. Journal of Foraminiferal Research, 19:38–51.Google Scholar
Hills, S. J., and Thierstein, H. R. 1989. Plio-Pleistocene calcareous plankton biochronology. Marine Micropaleontology, 14:67–96.CrossRefGoogle Scholar
Hopkins, R. H. 1990. Planktic foraminiferal correlation of the late Pliocene to early Pleistocene of DSDP Sites 284 and 593 (Challenger Plateau) with New Zealand stages. New Zealand Geological Survey Report PAL 149, 33 p.Google Scholar
Hornibrook, N. de B. 1980. Correlation of Pliocene biostratigraphy, magnetostratigraphy and δ18O fluctuation in New Zealand and DSDP Site 284. Newsletter of Stratigraphy, 9:114–120.Google Scholar
Hornibrook, N. de B. 1981. Globorotalia (planktonic Foraminiferida) in the late Pliocene and early Pleistocene of New Zealand. New Zealand Journal of Geology and Geophysics, 24:263–292.Google Scholar
Hornibrook, N. de B. 1982. Late Miocene to Pleistocene Globorotalia (Foraminiferida) from Deep Sea Drilling Project Leg 29, Site 284, Southwest Pacific. New Zealand Journal of Geology and Geophysics, 25:83–99.Google Scholar
Hornibrook, N. de B., Brazier, R. C., and Strong, C. P. 1989. Manual of New Zealand Permian to Pleistocene Foraminiferal Biostratigraphy. New Zealand Geological Survey Paleontological Bulletin 56, 175 p.Google Scholar
Lamboy, W. 1990. Disjoint principal component analysis: a statistical method of botanical identification. Systematic Botany, 15:3–12.Google Scholar
Maiya, S., Saito, T. and, Sato, T. 1976. Late Cenozoic planktonic foraminiferal biostratigraphy of northwest Pacific sedimentary sequences, p. 395–422. In Takayanagi, Y. and Saito, T. (eds.), Progress in Micropaleontology. American Museum of Natural History, New York.Google Scholar
Malmgren, B. A. 1979. Multivariate normality tests of planktonic foraminiferal data. Mathematical Geology, 11:285–297.CrossRefGoogle Scholar
Malmgren, B. A., and Kennett, J. P. 1982. The potential of morphometrically based phylo-zonation: application of a late Cenozoic foraminiferal lineage. Marine Micropaleontology, 7:285–296.Google Scholar
Ramos, L. S., Beebe, K. R., Carey, W. P., Sanchez, E., Erickson, B. C., Wilson, B. E., Wangen, L. E., and Kowalski, B. R. 1986. Chemometrics. Analytical Chemistry, 58:294R–315R.Google Scholar
Reyment, R. A. 1991. Multidimensional Paleobiology. Pergamon Press, Oxford, 377 + S39 p.Google Scholar
Reyment, R. A., Blackith, R. E., and Campbell, N. A. 1984. Multivariate Morphometrics, 2nd edition. Academic Press, New York, 233 p.Google Scholar
Rohlf, F. J. 1975. Generalization of the gap test for the detection of multivariate outliers. Biometrics, 31:93–101.Google Scholar
Saelen, G., and Karstang, T. V. 1989. Chemical signature of belemnites. Neues Jahrbuch für Geologie und Paläontologie, 177:333–346.Google Scholar
Sharaf, M. A., Illman, D. L., and Kowalski, B. R. 1986. Chemometrics. In Elving, P. J. and Winefordner, J. D. (eds.), Chemical Analysis, A Series of Monographs on Analytical Chemistry and its Application. John Wiley & Sons, New York, 330 p.Google Scholar
Wei, K.-Y. 1987a. Multivariate morphometric differentiation of chronospecies in the late Neogene planktonic foraminiferal lineage Globoconella. Marine Micropaleontology, 12:183–202.Google Scholar
Wei, K.-Y. 1987b. Tempo and mode of evolution in Neogene planktonic foraminifera: taxonomic and morphological evidence. Unpubl. Ph.D. dissertation, University of Rhode Island, Kingston, 397 p.Google Scholar
Wei, K.-Y. 1992. Reconstructing sea surface temperatures using pattern recognition technique: a case study of the Recent nannofossil assemblages in the South China Sea. International Nannoplankton Association Newsletter, 14:94–95.Google Scholar
Wei, K.-Y. 1994. Allometric heterochrony in the Pliocene-Pleistocene planktic foraminiferal clade Globoconella. Paleobiology, 20:66–84.Google Scholar
Wei, K.-Y., and Kennett, J. P. 1988. Phyletic gradualism and punctuated equilibrium in the late Neogene planktonic foraminiferal clade Globoconella. Paleobiology, 14:345–363.CrossRefGoogle Scholar
Wei, K.-Y., Zhang, Z.-W., and Wray, C. 1992. Shell ontogeny of Globorotalia inflata (I): growth dynamics and ontogenetic stages. Journal of Foraminiferal Research, 22:318–327.Google Scholar
Wold, H. 1966. Nonlinear estimation by iterative least squares procedures, p. 411–444. In David, F. N. (ed.), Research Papers in Statistics, Festschrift for J. Neyman. Wiley, New York.Google Scholar
Wold, H. 1982. Soft modeling: the basic design and some extensions, p. 1–54. In Jöreskog, H. and Wold, H. (eds.), Systems Under Indirect Observation. North-Holland, Amsterdam.Google Scholar
Wold, S. 1976. Pattern recognition by means of disjoint principal components models. Pattern Recognition, 8:127–139.Google Scholar
Wold, S. 1978. Cross validatory estimation of the number of components in factor and principal component models. Technometrics, 20:397–406.Google Scholar
Wold, S., and Sjöström, M. 1977. SIMCA: a model for analyzing chemical data in terms of similarity and analogy, p. 243–252. In Kowalski, B. R. (ed.), Chemometrics: Theory and Application. American Chemical Society Symposium Series, No. 52.Google Scholar
Wold, S., Albano, C., Dunn, W. J. III, Edlund, U., Esbensen, K., Geladi, P., Hellberg, S., Johansson, E., Lindberg, W., and Sjöström, M. 1984a. Multivariate data analysis in chemistry, p. 17–95. In Kowalski, B. R. (ed.), Chemometrics. Mathematics and Statistics in Chemistry. Reidel Publishing Company, Dordrecht, Holland.Google Scholar
Wold, S., Albano, C., Dunn, W. J. III, Edlund, U., Esbensen, K., Hellberg, S., Johansson, E., Lindberg, W., and Sjöström, M. 1984b. Modeling data tables by principal components and PLS: class patterns and quantitative predictive relations. Analusis, 12:477–485.Google Scholar