The attached dataset shows approximately 6000 saplings in approximately 50 variable sized forest gaps. I am interested in learning how these saplings are growing within their respective gaps (i.e. clustered, random, dispersed). As you know, a traditional approach would be to run Global Moran's I. However, aggregations of trees within aggregations of gaps seems to be an inappropriate use of Moran's I. I ran some test statistics with Moran's I using a threshold distance of 50 meters, which produced nonsensical results (i.e. p-value = 0.0000000...). The interaction among the gap aggregations are likely producing these results. I have considered creating a script to loop through individual canopy gaps and determine the clustering within each gap, although displaying these results to the public would be problematic. What is the best approach to quantify clustering within clusters?
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