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On choosing a resemblance measure for non-linear predictive ordination

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Part of the book series: Advances in vegetation science ((AIVS,volume 7))

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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R. K. Peet

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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

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-8939-5

  • Online ISBN: 978-94-009-5526-4

  • eBook Packages: Springer Book Archive

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