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Background  The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns ( global and local test ; (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA.    Results  We compared the performance of this new test to the original  global  and  local  tests using datasets simulated under classical population genetic models. Results show that our test outperforms the original  global  and  local  tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to test various sets of axes, it can be used to guide the selection of retained sPCA components.    Conclusions  As such, our test represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns.    Electronic supplementary material  The online version of this article (10.1186/s12859-017-1988-y) contains supplementary material, which is available to authorized users.

An Eigenvalue test for spatial principal component analysis