Canonical correspondence analysis with variation partitioning: some comments and an application

. This study presents an alternative treatment of data from a comprehensive vegetation study in which the main gradient structure of boreal coniferous forest vegetation in southern Norway was investigated by ordination techniques. The data sets include vegetation samples of different plot sizes, supplied with measurements of 33 environmental explanatory variables (classified in four groups) and nine spatial explanatory variables derived from geographical coordinates. Partitioning the variation of the species‐sample plot matrices on different sets of explanatory variables is performed by use of (partial) Canonical Correspondence Analysis. Several aspects of vegetation‐environment relationships in the investigation area are discussed on the basis of results obtained by the new method. Generally, ca. 35% of the variation in species abundances are explained by environmental and spatial variables. The results indicate support for the hypothesis of macro‐scale topographic control over the differentiation of the vegetation, more strongly so in pine than in spruce forest where soil nutrients play a major role. Towards finer scales, the primary topographical and topographically dependent factors lose importance, and vegetational differentiation is more strongly affected by the accumulated effects of the vegetation (including the tree stand) on soils, shading, litter fall, etc. The fraction of variation in species abundance explained by significant environmental variables was found to be ca. twice as large as the fraction explained by spatial variables. The fraction of variation explained by the supplied variables differed between data sets; it was lower for cryptogams 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 discussed: improvements, necessary precautions, and the advantages over alternative methods.