Performance of four ordination techniques assuming three different non-linear species response models

Austin, M. P.

doi:10.1007/bf00055298

Cited by 81 publications

(92 citation statements)

References 42 publications

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“…Based on presence/absence data and down-weighting of rare species, a non-metric multidimensional scaling (NMDS) ordination of the species composition of the three groups was performed using R software (version 2.9.0; R Development Core Team, 2009). Since NMDS is a measure of dissimilarity based on a monotonic transformation where the rank order and the distances between points of the original correlation matrix are preserved in the ordination (Austin, 1976;Kenkel & Orlóci, 1986;Whittaker, 1987), it represents an ideal tool to assess the spatial turnover. In order to measure the percentage differences between the considered groups, the Mann-Whitney U similarity was measured on presence/absence data.…”

Section: Species Changementioning

confidence: 99%

Thirty years unmanaged green roofs: Ecological research and design implications

Catalano

Marcenò

Laudicina

et al. 2016

Landscape and Urban Planning

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Section: Species Changementioning

confidence: 99%

Thirty years unmanaged green roofs: Ecological research and design implications

Catalano

Marcenò

Laudicina

et al. 2016

Landscape and Urban Planning

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“…General studies that compare proximity measures and their influence upon ordination and clustering are fairly common (e.g., Austin, 1976;Baroni-Urbani & Buser, 1976;Fasham, 1977;Gower & Legendre, 1986;Hubálek, 1982;Kenkel & Orloci, 1986). In addition, several specific studies have investigated the composition and use of proximity measures in IR and informetrics (e.g., Boyack, Klavans, & Börner, 2005;Ellis, Furner-Hines, & Willett, 1993;Gmür, 2003;Jones & Curtice, 1967;Jones & Furnas, 1987;Klavans & Boyack, 2006;Leydesdorff, 1987;McGill, Koll, & Noreault, 1979;Oberski, 1988;Wang, Wong, & Yao, 1992).…”

Section: Introductionmentioning

confidence: 96%

Matrix comparison, Part 1: Motivation and important issues for measuring the resemblance between proximity measures or ordination results

Schneider

Borlund

2007

J. Am. Soc. Inf. Sci.

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The present two-part article introduces matrix comparison as a formal means of evaluation in informetric studies such as cocitation analysis. In this first part, the motivation behind introducing matrix comparison to informetric studies, as well as two important issues influencing such comparisons, are introduced and discussed. The motivation is spurred by the recent debate on choice of proximity measures and their potential influence upon clustering and ordination results. The two important issues discussed here are matrix generation and the composition of proximity measures. The approach to matrix generation is demonstrated for the same data set, i.e., how data is represented and transformed in a matrix, evidently determines the behavior of proximity measures. Two different matrix generation approaches, in all probability, will lead to different proximity rankings of objects, which further lead to different ordination and clustering results for the same set of objects. Further, a resemblance in the composition of formulas indicates whether two proximity measures may produce similar ordination and clustering results. However, as shown in the case of the angular correlation and cosine measures, a small deviation in otherwise similar formulas can lead to different rankings depending on the contour of the data matrix transformed. Eventually, the behavior of proximity measures, that is whether they produce similar rankings of objects, is more or less dataspecific. Consequently, the authors recommend the use of empirical matrix comparison techniques for individual studies to investigate the degree of resemblance between proximity measures or their ordination results. In part two of the article, the authors introduce and demonstrate two related statistical matrix comparison techniques the Mantel test and Procrustes analysis, respectively. These techniques can compare and evaluate the degree of monotonicity between different proximity measures or their ordination results. As such, the Mantel test and Procrustes analysis can be used as statistical validation tools in informetric studies and thus help choosing suitable proximity measures.

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“…Correspondence analysis is useful in the investigation of cross-classified data (Greenacre, 1984), and is particularly useful for analysis of two-way tables that record species presence and absence (Hill, 1973(Hill, , 1974. The technique is now used frequently in ecological investigations, and comparative studies give it high marks for ordination (Hill, 1974;Fasham, 1977;Gauch et al, 1977;Del Moral, 1980), though certain limitations in its ability to express ecological gradients have been noted (Austen, 1976;Oksanen, 1983;Ezcurra, 1987). The technique has been widely applied for data description, simplification and exploratory analysis, in the tradition of principal components analysis and other multivariate procedures (e.g.…”

Section: Methodsmentioning

confidence: 97%