Large trends in French topsoil characteristics are revealed by spatially constrained multivariate analysis

Spatially constrained multivariate analysis methods (MULTISPATI-PCA) and classical principal component analysis are applied for the entire country of France to study the main soil characteristics of topsoil and to assess if their multivariate spatial pattern can provide insight on their extent and origin. The results of the MULTSPATI-PCA provided evidence of strong spatial structures attributed to different natural processes. The first axis was interpreted as an axis of global soil richness in clay content. Axis 2 reflected the influence of some parent materials on the geochemical content of K and Al. Axis 3 showed a very large gradient of relative content in coarse silt. Axis 4 was driven by gradients of maritime influence. We show that MULTISPATI-PCA allows better than classical PCA to detect and map large regional trends in the distribution of topsoil characteristics. The two first axes were expected and the maps obtained by both methods were consistent. Interestingly, the other gradients were not expected and were better shown by MULTISPATI-PCA than by classical PCA.

Research Highlights
► We apply spatially constrained multivariate analysis to a large national dataset.
► We map the main soil characteristics of topsoil.
► Their spatial distribution and correlation provide insight on their origin.
► The results evidence spatial structures attributed to different natural processes.
► These structures were better evidenced than using classical techniques.
► Some of these structures were unexpected, among which costal gradients.