Hostname: page-component-5c6d5d7d68-wp2c8 Total loading time: 0 Render date: 2024-08-22T11:14:18.920Z Has data issue: false hasContentIssue false
  • English
  • Français

Multivariate Methods of Analyzing Paleoecological Data

Published online by Cambridge University Press:  26 July 2017

Warren L. Kovach
Warren L. Kovach*
Affiliation:
Department of Biology, Indiana University, Bloomington, IN 47405
Get access

Extract

Multivariate analysis can be defined as an analysis in which many variables are examined simultaneously. These types of analyses are invaluable in many scientific fields in which patterns and trends in complex systems must be studied. Multivariate analysis is used in community ecology and paleoecology to summarize patterns of variation between and within different communities so as to allow the ecologist to describe the major patterns and interpret the cause of variation. Some examples of plant paleoecological studies which have used multivariate techniques are Oltz (1969, 1971), Clapham (1971), Robichaux and Taylor (1977), Pheifer (1979), Spicer and Hill (1979), Frederickson (1981), Phillips and DiMichele (1981), Boulter and Hubbard (1982), LaPasha and Miller (1984), Farley and Dilcher (1986), and Kovach (1985, 1987).

Type
Research Article
Information
The Paleontological Society Special Publications , Volume 3: Methods and Applications of Plant Paleoecology , 1988 , pp. 72 - 104
Copyright
Copyright © 1987 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

Beals, E. W., 1984. Bray-Curtis ordination: an effective strategy for analysis of multivariate ecological data. Advances in Ecological Research, 14:1–55.Google Scholar
Birks, H. J. B., and Deacon, J., 1973. A numerical analysis of the past and present flora of the British Isles. New Phytology, 72:877–902.Google Scholar
Birks, H. J. B., and Webb, T. III, and Berti, A. A., 1975. Numerical analysis of pollen samples from central Canada: a comparison of methods.Google Scholar
Boulter, M. C., and Hubbard, R. N. L. B., 1982. Objective paleoecological and biostratigraphic interpretation of Tertiary palynological data by multivariate statistical analysis. Palynology, 6:55–68.Google Scholar
Clapham, W. B. Jr., 1972. Numerical analysis and group formation in palynology. Geosci. Man, 4:73–85.Google Scholar
Clymo, R. S., 1980. Preliminary survey of the peat-bog Hummell Knowe Moss using various numerical methods. Vegetatio, 42:129–148.Google Scholar
Del Moral, R., 1980. On selecting indirect ordination methods. Vegetatio, 42: 75–84.Google Scholar
Farley, M. B., and Dilcher, D. L., 1986. Correlation between miospores and depositional environments of the Dakota Formation (mid-Cretaceous) of north-central Kansas and adjacent Nebraska, U.S.A. Palynology, 10:117–133.Google Scholar
Fasham, M. J. R., 1977. A comparison of nonmetric multidimensional scaling, principle components and reciprocal averaging for the ordination of simulated coenoclines and coenoplanes. Ecology, 58:551–561.Google Scholar
Fredericksen, N. O., 1981. Middle Eocene to Early Oligocene plant communities of the Gulf Coast. In: Gray, J. et al. (eds.), Communities of the Past. Hutchinson Ross Publishing Co., Stroudsburg, Pennsylvania, pp.493–549.Google Scholar
Gauch, H. G. Jr., 1982. Multivariate analysis in community ecology. Cambridge University Press, New York. 298pp.Google Scholar
Gauch, H. G. Jr., and Whittaker, R. H., 1972a. Coenocline simulation. Ecology, 53: 446–451.Google Scholar
Gauch, H. G. Jr., and Whittaker, R. H., 1972b. Comparison of ordination techniques. Ecology, 53:868–875.Google Scholar
Gauch, H. G. Jr., Whittaker, R. H., and Wentworth, T. R., 1977. A comparative study of reciprocal averaging and other ordination techniques. J. Ecol., 65:157–174.Google Scholar
Gauch, H. G. Jr., Whittaker, R. H., and Singer, S. B., 1981. A comparative study of nonmetric ordinations. Journal of Ecology, 69:135–152.Google Scholar
Gordan, A. D., and Birks, H. J. B., 1972. Numerical methods in Quaternary palaeoecology. I. Zonation of pollen diagrams. New Phytology, 71:961–979.Google Scholar
Gordan, A. D., and Birks, H. J. B., 1974. Numerical methods in Quaternary palaeoecology. II. Comparison of pollen diagrams. New Phytology, 73:221–249.Google Scholar
Greig-Smith, P., 1980. The development of numerical classification and ordination. Vegetatio, 42:1–9.Google Scholar
Greig-Smith, P., 1983. Quantitative Plant Ecology. University of California Press, Los Angeles, 359pp.Google Scholar
Hill, M. O., and Gauch, H. G. Jr., 1980. Detrended correspondence analysis: an improved ordination technique.Google Scholar
Kenkel, N. C., and Orloci, L., 1986. Applying metric and nonmetric multidimensional scaling to ecological studies: some new results. Ecology, 67:919–928.Google Scholar
Kovach, W. L., 1985. A Cretaceous megaspore flora from the Dakota Formation of Kansas. American Journal of Botany, 72(6):895.Google Scholar
Kovach, W. L., 1987. Dispersed plant remains from the Cenomanian of Kansas: Floristic and paleoecologic approaches. , Indiana University.Google Scholar
Kruskal, J. B., and Wish, M., 1976. Multidimensional scaling. Sage University Paper series on Quantitative Applications in the Social Sciences, series number 07–011, Beverly Hills. 93pp.Google Scholar
LaPasha, C. A., and Miller, C. N. Jr., 1984. Flora of the Early Cretaceous Kootenai Formation in Montana, paleoecology. Palaeont. Abt. B., 194:109–130.Google Scholar
Legendre, L., and Legendre, P., 1983. Numerical ecology. Elsevier Scientific Publishing Co., New York.Google Scholar
Norton, D. A., McGlone, M. S., and Wigley, T. M. L., 1986. Quantitative analyses of modern pollen-climate relationships in New Zealand indigenous forests. New Zealand Journal of Botany, 24:331–342.Google Scholar
Oltz, D. F. Jr., 1969. Numerical analyses of palynological data from Cretaceous and Early Tertiary sediments in east central Montana. Palaeont. Abt. B, 128:90–166.Google Scholar
Oltz, D. F. Jr., 1971. Cluster analyses of Late Cretaceous-Early Tertiary pollen and spore data. Micropaleontology 17:221–232.Google Scholar
Orloci, L., 1978. Multivariate analysis in vegetation research. Dr. W. Junk B.V. Publishers, Boston. 451pp.Google Scholar
Pheifer, R. N., 1979. The paleobotany and paleoecology of the unnamed shale overlying the Danville Coal Member (VII) in Sullivan County, Indiana. , Indiana University. 295 pp.Google Scholar
Phillips, T. L., and DiMichele, W. A., 1981. Paleoecology of Middle Pennsylvanian age coal swamps in southern Illinois/Herrin Coal Member at Sahara Mine No. 6. In: Niklas, K. J. (ed), Paleobotany, Paleoecology, and Evolution. Praeger Publishers, New York pp.231–284.Google Scholar
Pielou, E. C., 1984. The interpretation of ecological data. John Wiley and Sons, New York. 263pp.Google Scholar
Prentice, I. C., 1977. Non-metric ordination methods in ecology. Journal of Ecology, 65:85–94.Google Scholar
Prentice, I. C., 1980a. Multidimensional scaling as a research tool in Quaternary palynology: a review of theory and methods. Reviews Palaeobotany and Palynology, 31: 71–104.Google Scholar
Prentice, I. C., 1980b. Vegetation analysis and order invariant gradient models. Vegetatio, 42:27–34.Google Scholar
Robichaux, R. H., and Taylor, D. W., 1977. Vegetation-analysis techniques applied to Late Tertiary fossil floras from the western United States. Journal of Ecology, 65:643–660.Google Scholar
Sneath, P. H. A., and Sokal, R. R., 1973. Numerical Taxonomy. W. H. Freeman and Co., San Francisco. 573pp.Google Scholar
Sokal, R. R., and Rohlf, F. J., 1981. Biometry. W. H. Freeman and Co., San Francisco. 859pp.Google Scholar
Spicer, R. A., and Hill, C. R., 1979. Principle components and correspondence analyses of quantitative data from a Jurassic plant bed. Reviews Palaeobotany Palynology 28:273–299.Google Scholar