The interactions between plants of different species, age or size play an important role in the dynamics of an ecosystem and can induce specific structures. These interactions can be studied by analysing the spatial structure of the corresponding bivariate patterns. The intertype L12-function has recently been successfully used in many papers for that purpose. However, when interpreting the results obtained with ecological data, at least two different null hypotheses – independence or random labelling – can be appropriate, depending on the context of the study and the nature of the data. As these two hypotheses correspond to different confidence intervals, an inappropriate choice of the null hypothesis can lead to misinterpretations of biotic interactions when studying ecological data. This problem has rarely been mentioned in the literature.
In this paper we clarify the differences between these two null hypotheses, and illustrate the risk of misinterpretation when using an inappropriate null hypothesis. We review the main characteristics of these two hypotheses, and analyse the spatial structure of both real data from forest stands and simulated virtual stands of different structures. We demonstrate that the risk of misinterpretation is quite high, and that extreme misinterpretations, i.e. cases leading to opposite conclusions in terms of spatial interaction, can occur in a significant number of cases. We therefore propose some guidelines to help ecologists avoid such misinterpretations.
Abbreviation: CSR = Complete spatial randomness.