Methods for coupling two data sets (species composition and environmental variables for example) are well known and often used in ecology. All these methods require that variables of the two data sets have been recorded at the same sample stations. But if the two data sets arise from different sample schemes, sample locations can be different. In this case, scientists usually transform one data set to conform with the other one that is chosen as a reference. This inevitably leads to some loss of information. We propose a new ordination method, named spatial-RLQ analysis, for coupling two data sets with different spatial sample techniques. Spatial-RLQ analysis is an extension of co-inertia analysis and is based on neighbourhood graph theory and classical RLQ analysis. This analysis finds linear combinations of variables of the two data sets which maximize the spatial cross-covariance. This provides a co-ordination of the two data sets according to their spatial relationships. A vegetation study concerning the forest of Chizé (western France) is presented to illustrate the method.
Abbreviations: CA = Correspondence analysis; CCA = Canonical Correspondence Analysis; GIS = Geographic Information System; GSVD = Generalized singular value decomposition; PCA = Principal Component Analysis; RDA = Redundancy analysis.
Nomenclature: Rameau et al. (1989).