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Multiscale ordination: a method for detecting pattern at several seales

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Abstract

Multivariate analyses of vegetation data have been restricted to a single scale of sampling, or multiscale sampling has been restricted to a single species. However, vegetation scientists need to be able to explore spatial relationships of many species over many scales. We present a modification of Noy-Meir & Anderson's (1971) method of multiscale ordination by summing two-term local covariance matrices and smoothing the component profiles. The advantages of our method are: 1) results are less subject to the starting position of the transect, 2) matrices may be added at any block size, and 3) plots of factor scores are smoothed by a moving weighted average to better reveal patterns at a prescribed scale.

This procedure provides statistical associations of species over a range of scales. The scales which exhibit the association to the maximum extent are then determined from multiscale ordination. The relationships of different associations and their scales can then be examined. The application of the method to fabricated data proved successful in recovering the structure built into the data. When used on real vegetation data, from a community and a landscape, the method revealed the details of species associations over a range of scales, and of the relationships among associations.

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Abbreviations

PCA =:

Principal Components Analysis

DCA =:

Detrended Correspondence Analysis

TTLC =:

Two-Term Local Covariance

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Authors and Affiliations

  1. Botany Department, Iowa State University, 50011-1020, Ames, IA, USA

    Jay M. Ver Hoef & David C. Glenn-Lewin

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  1. Jay M. Ver Hoef
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  2. David C. Glenn-Lewin
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Ver Hoef, J.M., Glenn-Lewin, D.C. Multiscale ordination: a method for detecting pattern at several seales. Vegetatio 82, 59–67 (1989). https://doi.org/10.1007/BF00217983

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  • DOI: https://doi.org/10.1007/BF00217983

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