Vegetation ordination is usually based on classical data reduction techniques such as principal components analysis, correspondence analysis, or multidimensional scaling. The usual methods do not account for multiscale correlations among species. In this paper, we use a geostatistical method, known as multivariate factorial kriging, for studying multiple-scale correlations. The case study was carried out in a mixed broadleaf forest of central Spain. Six tree species were included in the analysis. Data analysis included (i) experimental variogram calculation and modeling with the use of the linear model of coregionalization, (ii) principal components analysis, and (iii) cokriging. The results indicate that correlations among species are different depending on the spatial scale. We conclude that competition for light is the main factor controlling the spatial distribution of species at the plot-level scale of variation. At larger scales of variation, soil conditions and (or) human intervention are the key factors in determining the observed vegetation pattern. Based on the factor scores for the largest scale of variation, we conducted a cluster analysis to identify plots with similar characteristics. The resulting clusters have the remarkable property of being spatially continuous.
A case study on geochemical anomaly identification through principal components analysis supplementary projection
