Abstract
The development of non-linear ordination techniques has stemmed in part from work suggesting that species behave non-linearly to changing environmental factors or gradients. Developments in this area can be seen in two related phases: new algorithms, and the incorporation of new resemblance measures. Emphasis in this paper is placed on resemblance measures incorporated into a method of multi-dimensional scaling. The results show that a resemblance measure which reflects the non-linearities of the data can produce significant improvement in ordination, if the standardizations have not been too ‘severe’.
One of the authors (L. Orlóci) was a recipient of an N.S.E.R.C. grant during the tenure of this project. The authors wish to thank C. Brambilla and G. Salzano for the use of their computer program. A copy of a modified version used here may be obtained from the first author at no charge.
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References
Austin, M. P., 1976. Performance of four ordination techniques assuming three different non-linear species response models. Vegetatio 33: 43–49.
Austin, M. P., 1979. Current approaches to the non-linearity problem in vegetation analysis. In: Patil, G. P. & Rosenzweig, M. L. (eds.), Contemporary Quantitative Ecology and Related Ecometrics, pp. 197–210. ICPH, Fairland, Maryland.
Austin, M. P., 1980. Searching for a model for use in vegetation analysis. Vegetatio 42: 11–21.
Brambilla, C. & Salzano, G, 1981. A non-metric multi-dimensional scaling method for non-linear dimension reduction. Istituto per le Applicazioni del Calcólo ‘Mauro Picone’. Quaderni, Serie III - N. 121. 35 pp.
Fasham, M.J. R., 1977. A comparison of nonmetric multidimensional scaling, principal components and reciprocal averaging for the ordination of simulated coenoclines, and coenoplanes. Ecology 58: 551–561.
Feoli, D. & Feoli Chiapella, L., 1980. Evaluation of ordination methods through simulated coenoclines: some comments. Vegetatio 42: 35–41.
Forsythe, W. L. & Loucks, O. L., 1972. A transformation for species response to habitat factors. Ecology 53: 1112–1119.
Gauch, H. G., 1973. The relationship between sample similarity and ecological distance. Ecology 54: 618–622.
Gauch, H. G., Chase, G. B. & Whittaker, R. H., 1974. Ordination of vegetation samples by Gaussian species distributions. Ecology 55: 1382–1390.
Gauch, H. G. & Whittaker, R. H., 1972. Comparison of ordination techniques. Ecology 53: 868–875.
Groenewoud, H. van, 1965. Ordination and classification of Swiss and Canadian coniferous forests by various biometric and other methods. Ber. Geobot. Inst. ETH Stiftg. Riibel, Zürich 36: 28–102.
Hill, M. O. & Gauch, H. G., 1980. Detrended correspondence analysis: an improved ordination technique. Vegetatio 42: 47–58.
Ihm, P. & van Groenewoud, H., 1975. A multivariate ordering of vegetation data based on Gaussian type gradient response curves. J. Ecol. 63: 767–777.
Johnson, R., 1973. A study of some multivariate methods for the analysis of botanical data. Ph.D. dissertation, Utah State Univ., Logan, Utah. Johnson, R. W. & Goodall, D. W., 1980. A maximum likelihood approach to non-linear ordination. Vegetatio 41: 133–142.
Kendall, D. G., 1971. Seriation from abundance matrices. In: Hodson, F. R., Kendall, D. G. & Tautu, P. (eds.), Mathematics in the Archaeological and Historical Sciences, pp. 215–252. Edinburgh Univ. Press, Edingburgh.
Kruskal, J. B., 1964a. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29: 1–27.
Kruskal, J. B., 1964b. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29: 115–129.
Noy-Meir, I. & Austin, M. P., 1970. Principal component ordination and simulated vegetational data. Ecology 51: 551–552.
Orloci, L., 1978. Multivariate Analysis in Vegetation Research. 2nd ed. Junk, The Hague. 451 pp.
Orloci, L., 1980. An algorithm for predictive ordination. Vegetatio 42: 23–25.
Phillips, D. L., 1978. Polynomial ordination: field and computer simulation testing of a new method. Vegetatio 37: 129–140.
Phillips, D. L., 1978. Polynomial ordination: field and computer simulation testing of a new method. Vegetatio 37: 129–140.
Whittaker, R. H., 1956. Vegetation of the Great Smoky Mountains. Ecol. Monog. 26: 1–80.
Whittaker, R. H., 1967. Gradient analysis of vegetation. Biol. Rev. 42: 207–264.
Whittaker, R. H., 1972. Evolution and measurement of species diversity. Taxon 21: 213–251.
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© 1985 Dr W. Junk Publishers, Dordrecht
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Fewster, P.H., Orlóci, L. (1985). On choosing a resemblance measure for non-linear predictive ordination. In: Peet, R.K. (eds) Plant community ecology: Papers in honor of Robert H. Whittaker. Advances in vegetation science, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-5526-4_6
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DOI: https://doi.org/10.1007/978-94-009-5526-4_6
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