Abstract
Individual differences scaling is a multidimensional scaling method for finding a common ordination for several data sets. An individual ordination for each data set can then be derived from the common ordination by adjusting the axis lengths so as to maximize the correlations between observed proximities and individual ordination distances. The importance of the various axes for each data set and the mutual similarities and goodness of fit for the individual data sets are described by weight plots. As an example, 46 soft-water lakes in eastern Finland are ordinated on two dimensions according to 3 chemical data sets (water in summer and autumn, sediment) and 4 biological sets (major phytoplankton groups, phytoplankton, surface sediment diatom and cladoceran assemblages). The method seems to be effective as a means of ordination for obtaining the common ordination for the data sets. The major taxonomic groups gave the ordination which differed most clearly from the ordinations of the other data sets. Phytoplankton was most poorly ordinated in all the analyses. The other data sets were fairly coherent. When only biological data sets were ordinated, the diatoms and cladocerans showed rather different patterns. It seems that the cladocerans are best correlated with water chemistry, both according to weights in the joint analysis, and according to correlation between the axes from the biological data sets and the chemical variables.
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Abbreviations
- CCA:
-
Canonical correspondence analysis
- IDS:
-
Individual differences scaling
- MDS:
-
multidimensional scaling
- PCA:
-
Principal components analysis
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Oksanen, J., Huttunen, P. (1990). Finding a common ordination for several data sets by individual differences scaling. In: Grabherr, G., Mucina, L., Dale, M.B., Ter Braak, C.J.F. (eds) Progress in theoretical vegetation science. Advances in vegetation science, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-1934-1_11
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DOI: https://doi.org/10.1007/978-94-009-1934-1_11
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