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Multivariate analysis in weed science research

Published online by Cambridge University Press:  20 January 2017

N. C. Kenkel ,
D. A. Derksen ,
A. G. Thomas  and
P. R. Watson
D. A. Derksen
Affiliation:
Agriculture and Agri-Food Canada, P.O. Box 1000F, R.R. #3, Brandon, Manitoba, Canada R7A 5Y3
A. G. Thomas
Affiliation:
Agriculture Canada Research Station, 107 Science Cres., Saskatoon, Saskatchewan, Canada S7N 0X2
P. R. Watson
Affiliation:
Agriculture and Agri-Food Canada, P.O. Box 1000F, R.R. #3, Brandon, Manitoba, Canada R7A 5Y3
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Abstract

Data containing many variables are often collected in weed science research, but until recently few weed scientists have used multivariate statistical methods to examine such data. Multivariate analysis can be used for both descriptive and predictive modeling. This paper provides an intuitive geometric introduction to the more commonly used and relevant multivariate methods in weed science research, including ordination, discriminant analysis, and canonical analysis. These methods are illustrated using a simple artificial data set consisting of abundance measures of six weed species and two soil variables over 12 sample plots.

Keywords

Canonical correlation analysis (CANCOR)canonical correspondence analysis (CCA)canonical discriminant analysis (CDA)correspondence analysis (CA)nonmetric multidimensional scaling (NMDS)ordination analysis, principal component analysis (PCA)principal coordinate analysis (PCoA)redundancy analysis (RDA)statistical methods

Information

Type
Review
Information
Weed Science , Volume 50 , Issue 3 , June 2002 , pp. 281 - 292
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bàrberi, P., Cozzani, A., Macchia, M., and Bonari, E. 1998. Size and composition of the weed seedbank under different management systems for continuous maize cropping. Weed Res. 38:319–334.CrossRefGoogle Scholar
Bàrberi, P., Silvestri, N., and Bonari, E. 1997. Weed communities of winter wheat as influenced by input level and rotation. Weed Res. 37:301–313.Google Scholar
Benzecri, J.-P. 1992. Correspondence Analysis Handbook. New York: Marcel Dekker. 665 p.Google Scholar
Bradfield, G. E. and Kenkel, N. C. 1987. Nonlinear ordination using flexible shortest path adjustment of ecological distances. Ecology 68:750–753.Google Scholar
Chambers, J. M., Cleveland, W. S., Kleiner, B., and Tukey, P. A. 1983. Graphical Methods for Data Analysis. Belmont, CA: Wadsworth. 395 p.Google Scholar
Cochran, W. G. 1977. Sampling Techniques. 3rd ed. New York: J. Wiley. 428 p.Google Scholar
Conn, J. S. and Delapp, J. A. 1983. Weed species shifts with increasing field age in Alaska. Weed Sci. 31:520–524.Google Scholar
Dale, M.R.T., Thomas, A. G., and Johns, E. A. 1992. Environmental factors including management practices as correlates of weed community composition in spring seeded crops. Can. J. Bot. 70:1931–1939.CrossRefGoogle Scholar
Del la Fuente, E. B., Suárez, S. A., Ghersa, C. M., and León, R.J.C. 1999. Soybean weed communities: relationships with cultural history and crop yield. Agron. J. 91:234–241.Google Scholar
Derksen, D. A., Lafond, G. P., Thomas, A. G., Loeppky, H. A., and Swanton, C. J. 1993. Impact of agronomic practices on weed communities: tillage systems. Weed Sci. 41:409–417.CrossRefGoogle Scholar
Derksen, D. A., Thomas, A. G., Lafond, G. P., Loeppky, H. A., and Swanton, C. J. 1995. Impact of herbicides on weed community diversity within conservation-tillage systems. Weed Res. 35:311–320.Google Scholar
Derksen, D. A., Watson, P. R., and Loeppky, H. A. 1998. Weed community composition in seedbanks, seedlings and mature plant communities in a multi-year trial in western Canada. Asp. Appl. Biol. 41:43–50.Google Scholar
Dieleman, J. A., Mortensen, D. A., Buhler, D. D., Cambardella, C. A., and Moorman, T. B. 2000a. Identifying associations among site properties and weed species abundance. I. Multivariate analysis. Weed Sci. 48:567–575.Google Scholar
Dieleman, J. A., Mortensen, D. A., Buhler, D. D., and Ferguson, R. B. 2000b. Identifying associations among site properties and weed species abundance. II. Hypothesis generation. Weed Sci. 48:576–587.Google Scholar
Digby, P.G.N. and Kempton, R. A. 1987. Multivariate Analysis of Ecological Communities. New York: Chapman and Hall. 206 p.Google Scholar
Gabriel, K. R. 1981. Biplot display of multivariate matrices for inspection of data and diagnosis. Pages 147–173 In Barnett, V., ed. Interpreting Multivariate Data. Chichester: J. Wiley.Google Scholar
Gittins, R. 1985. Canonical Analysis: A Review with Applications in Ecology. New York: Springer. 351 p.Google Scholar
Goodall, D. W. 1954. Objective methods for the classification of vegetation. II. An essay in the use of factor analysis. Aust. J. Bot. 2:304–324.Google Scholar
Gower, J. C. 1966. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338.Google Scholar
Green, R. H. 1979. Sampling Design and Statistical Methods for Environmental Biologists. New York: J. Wiley. 257 p.Google Scholar
Green, R. H. 1993. Relating two sets of variables in environmental studies. Pages 149–163 In Patil, G. P. and Rao, C. R., eds. Multivariate Environmental Statistics. New York: Elsevier.Google Scholar
Greenacre, M. J. 1984. Theory and Applications of Correspondence Analysis. New York: Academic Press. 364 p.Google Scholar
Hill, M. O. 1973. Reciprocal averaging: an eigenvector method of ordination. J. Ecol. 61:237–249.Google Scholar
Hill, M. O. and Gauch, H. G. 1980. Detrended correspondence analysis, an improved ordination technique. Vegetatio 42:47–58.CrossRefGoogle Scholar
Jackson, D. A. 1993. Stopping rules in principal component analysis: a comparison of heuristical and statistical approaches. Ecology 74:2204–2214.CrossRefGoogle Scholar
Jackson, D. A. and Somers, K. M. 1991. Putting things in order: the ups and downs of detrended correspondence analysis. Am. Nat. 137:704–712.CrossRefGoogle Scholar
Jeffers, J.N.R. 1978. An Introduction to Systems Analysis: With Ecological Applications. London: University Park Press. 198 p.Google Scholar
Jeffers, J.N.R. 1982. Modeling. New York: Chapman and Hall. 80 p.Google Scholar
Jeffers, J.N.R. 1988. Practitioner's Handbook on the Modeling of Dynamic Change in Ecosystems. New York: J. Wiley. 181 p.Google Scholar
Kenkel, N. C. and Orlóci, L. 1986. Applying metric and nonmetric multidimensional scaling to ecological studies: some new results. Ecology 67:919–928.Google Scholar
Leeson, J. Y., Sheard, J. W., and Thomas, A. G. 1999. Multivariate classification of farming systems for use in integrated pest management studies. Can. J. Plant Sci. 79:647–654.CrossRefGoogle Scholar
Leeson, J. Y., Sheard, J. W., and Thomas, A. G. 2000. Weed communities associated with arable Saskatchewan farm management systems. Can. J. Plant Sci. 80:177–185.CrossRefGoogle Scholar
Legendre, P. and Legendre, L. 1998. Numerical Ecology. 2nd ed. Amsterdam: Elsevier. 853 p.Google Scholar
Légère, A. and Samson, N. 1999. Relative influence of crop rotation, tillage, and weed management on weed associations in spring barley cropping systems. Weed Sci. 47:112–122.CrossRefGoogle Scholar
McCloskey, M., Firbank, L. G., Watkinson, A. R., and Webb, D. J. 1996. The dynamics of experimental arable weed communities under different management practices. J. Veg. Sci. 7:799–808.CrossRefGoogle Scholar
McCune, B. 1997. Influence of noisy environmental data on canonical correspondence analysis. Ecology 78:2617–2623.CrossRefGoogle Scholar
Morrison, D. F. 1990. Multivariate Statistical Methods. 3rd ed. New York: McGraw-Hill. 495 p.Google Scholar
O’Donovan, J. T., McAndrew, D. W., and Thomas, A. G. 1997. Tillage and nitrogen influence weed population dynamics in barley (Hordeum vulgare). Weed Technol. 11:502–509.Google Scholar
Oksanen, J. and Minchin, P. R. 1997. Instability of ordination results under changes in input order: explanations and remedies. J. Veg. Sci. 8:447–454.Google Scholar
Ominski, P. D., Entz, M. H., and Kenkel, N. C. 1999. Weed supression by Medicago sativa in subsequent cereal crops: a comparative survey. Weed Sci. 47:282–290.Google Scholar
Orlóci, L. 1966. Goemetric models in ecology. I. The theory and application of some ordination methods. J. Ecol. 54:193–215.Google Scholar
Orlóci, L. 1978. Multivariate Analysis in Vegetation Research. The Hague: Junk. 451 p.Google Scholar
Palmer, M. W. 1993. Putting things in even better order: the advantages of canonical correspondence analysis. Ecology 74:2215–2230.Google Scholar
Podani, J. 1994. Multivariate Data Analysis in Ecology and Systematics. The Hague: SPB. 316 p.Google Scholar
Post, B. J. 1988. Multivariate analysis in weed science. Weed Res. 28:425–430.Google Scholar
Rao, C. R. 1964. The use and interpretation of principal component analysis in applied research. Sankhya Ser. A 26:329–358.Google Scholar
ter Braak, C.J.F. 1987. The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio 69:69–77.Google Scholar
ter Braak, C.J.F. 1995. Ordination. Pages 91–173 In Jongman, R.H.G., ter Braak, C.J.F., and van Tongeren, O.F.R., eds. Data Analysis in Community and Landscape Ecology. 2nd ed. Cambridge: Cambridge University Press.Google Scholar
ter Braak, C.J.F. and Prentice, I. C. 1988. A theory of gradient analysis. Adv. Ecol. Res. 18:271–317.Google Scholar
Thomas, A. G. and Frick, B. L. 1993. Influence of tillage systems on weed abundance in southwestern Ontario. Weed Technol. 7:699–705.Google Scholar
Tukey, J. W. 1977. Exploratory Data Analysis. Reading, MA: Addison-Wesley. 688 p.Google Scholar
van den Wollenberg, A. L. 1977. Redundancy analysis: an alternative to canonical correlation analysis. Psychometrika 42:207–219.CrossRefGoogle Scholar
Zar, J. H. 1974. Biostatistical Analysis. Englewood Cliffs, NJ: Prentice-Hall. 620 p.Google Scholar