CANONICAL CORRESPONDENCE-ANALYSIS WITH VARIATION PARTITIONING - SOME COMMENTS AND AN APPLICATION

This study presents an alternative treatment of data from a comprehens ive vegetation study in which the main gradient structure of boreal co niferous forest vegetation in southern Norway was investigated by ordi nation techniques. The data sets include vegetation samples of differe nt plot sizes, supplied with measurements of 33 environmental explanat ory variables (classified in four groups) and nine spatial explanatory variables derived from geographical coordinates. Partitioning the var iation of the species-sample plot matrices on different sets of explan atory variables is performed by use of (partial) Canonical Corresponde nce Analysis. Several aspects of vegetation-environment relationships in the investigation area are discussed on the basis of results obtain ed by the new method. Generally, ca. 35 % of the variation in species abundances are explained by environmental and spatial variables. The r esults indicate support for the hypothesis of macro-scale topographic control over the differentiation of the vegetation, more strongly so i n pine than in spruce forest where soil nutrients play a major role. T owards finer scales, the primary topographical and topographically dep endent factors lose importance, and vegetational differentiation is mo re strongly affected by the accumulated effects of the vegetation (inc luding the tree stand) on soils, shading, litter fall, etc. The fracti on of variation in species abundance explained by significant environm ental variables was found to be ca. twice as large as the fraction exp lained by spatial variables. The fraction of variation explained by th e supplied variables differed between data sets; it was lower for cryp togams than for vascular plants, and lower for smaller than for larger sample plots. Possible reasons for these patterns are discussed. Some methodological aspects of CCA with variation partitioning are discuss ed: improvements, necessary precautions, and the advantages over alter native methods.