Table of Contents
ISRN Ecology
Volume 2013 (2013), Article ID 753475, 9 pages
Research Article

A Statistical Test for Ripley’s Function Rejection of Poisson Null Hypothesis

1AgroParisTech, UMR EcoFoG, BP 709, French Guiana, 97310 Kourou, France
2AgroParisTech, UMR 518 Math. Info. Appli., 75005 Paris, France
3INRA, UMR 518 Math. Info. Appli., 75005 Paris, France

Received 10 January 2013; Accepted 14 March 2013

Academic Editors: U. M. Azeiteiro and J.-P. Rossi

Copyright © 2013 Eric Marcon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ripley’s 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 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.