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1 October 2003 Partitioning the variation in a plot-by-species data matrix that is related to n sets of explanatory variables
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Abstract
Variation partitioning by (partial) constrained ordination is a popular method for exploratory data analysis, but applications are mostly restricted to simple ecological questions only involving two or three sets of explanatory variables, such as climate and soil, this because of the rapid increase in complexity of calculations and results with an increasing number of explanatory variable sets. The existence is demonstrated of a unique algorithm for partitioning the variation in a set of response variables on n sets of explanatory variables; it is shown how the 2n − 1 non-overlapping components of variation can be calculated. Methods for evaluation and presentation of variation partitioning results are reviewed, and a recursive algorithm is proposed for distributing the many small components of variation over simpler components. Several issues related to the use and usefulness of variation partitioning with n sets of explanatory variables are discussed with reference to a worked example.Abbreviations: AVE = Average variation explained; CCA = Canonical Correspondence Analysis; CO = Constrained ordination; DCA = Detrended Correspondence Analysis; IU = Inertia units; pCO = Partial constrained ordination; RDA = Redundancy Analysis; TI = Total inertia; TVE = Total variation explained; VE = Variation explained.
and Økland Rune Halvorsen "Partitioning the variation in a plot-by-species data matrix that is related to n sets of explanatory variables," Journal of Vegetation Science 14(5), (1 October 2003). https://doi.org/10.1658/1100-9233(2003)014[0693:PTVIAP]2.0.CO;2
Received: 9 December 2002; Accepted: 25 February 2003; Published: 1 October 2003
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