1964
DOI: 10.1007/bf02289565
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Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis

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Cited by 6,285 publications
(3,198 citation statements)
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References 13 publications
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“…The STATISTICA package (StatSoft Inc., Tulsa, OK, USA) was used for multi-dimensional scaling (MDS) analysis. 29 BSPs were produced from the coding region sequences (positions 577-16023) using MCMC sampling in version 5.1 of the program BEAST. 30 The plots were obtained with a piecewise linear model and ancestral gene trees were based on a general time-reversible substitution model with invariant sites (GTR+I).…”
Section: Resultsmentioning
confidence: 99%
“…The STATISTICA package (StatSoft Inc., Tulsa, OK, USA) was used for multi-dimensional scaling (MDS) analysis. 29 BSPs were produced from the coding region sequences (positions 577-16023) using MCMC sampling in version 5.1 of the program BEAST. 30 The plots were obtained with a piecewise linear model and ancestral gene trees were based on a general time-reversible substitution model with invariant sites (GTR+I).…”
Section: Resultsmentioning
confidence: 99%
“…Occurrence of pest species was compared first with an ordination method, non-metric multidimensional scaling (NMS, Kruskal, 1964), and then with a multiple response permutation procedure (MRPP, Zimmerman et al, 1985). Possible associations between taxa and habitats were defined using indicator species analysis (ISA, Dufrene and Legendre, 1997).…”
Section: Resultsmentioning
confidence: 99%
“…To make the intensive plot data set parallel to the rapid assessment data, we normalized the intensive sample plot data to only include trees, shrubs, and up to three dominant herbaceous species in each plot. We then ran ordination analyses using nonmetric multidimensional scaling (NMS; Kruskal, 1964) with Sørensen distance, first on all the plots, then just on the intensively sampled plots. The cluster analysis and ordinations were performed in PC‐ORD 5.0 (McCune & Mefford, 1999).…”
Section: Methodsmentioning
confidence: 99%