Ripley’s K function is the classical tool to characterize the spatial structure of point patterns. It is widely used in vegetation studies. Testing its values against a null hypothesis usually relies on Monte-Carlo simulations since little is known about its distribution. We introduce a statistical test against complete spatial randomness (CSR). The test returns the P value to reject the null hypothesis of independence between point locations. It is more rigorous and faster than classical Monte-Carlo simulations. We show how to apply it to a tropical forest plot. The necessary R code is provided.
Marcon, E., Traissac, S., & Lang, G. (2013). A Statistical Test for Ripley’s K Function Rejection of Poisson Null Hypothesis . ISRN Ecology, 2013, 1–9. https://doi.org/10.1155/2013/753475