## Dray S., Pettorelli N., Chessel D. (2002).
*Matching data sets from two different spatial samples*.
Journal of Vegetation Science, 13:867--874.

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.

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