Fig. 1

Results from the spatial principal component analysis (sPCA) on the Hermann’s tortoise Testudo hermanni in the Italian Peninsula. (a-b) projection of the individual scores from the first two principal components on a distribution map; the color of the boxes (black or white) corresponds to the sign of the score and the area is proportional to the absolute value of the score; therefore large circles indicate large differentiation with smaller circles indicating smaller differences; (c) lagged scores of the first two axes together in a single colour plot, so that individuals that are closely related in the multivariate space are more similar in colour; (d) barplot showing eigenvalues, positive eigenvalues (red, on the left) correspond to global structures, while negative eigenvalues (blues, on the right) indicate local patterns