Hi there,I have a dataset consisting of measurements of tree size taken in more than 200 forest plots along a regular grid within a forest. For each plot, i can build summary statistics such as frequency distributions of such characters (e.g., height, diameters, etc). I have the expectation that such distribution would conform to a theoretical distribution at a very large spatial scale and indeed if I aggregate all data for all plots, my empirical curve fits the expected curve rather well. My objective is to detect at what spatial scale would such a theoretical prediction begin to break down, i.e., if I begin to disaggregate the data to include all the (n-1) plots close to each other, all the (n-2), etc., down to statistics collected independently for each plot, when does the mean distribution calculated for all possible combinations at that scale begin to depart significantly from the expected curve. I cannot figure out how to approach the problem. It is not point pattern, nor does it seem to be geostatistics (building a semi variogram would help but only partially). The question is really about data aggregation at different spatial scales for plots that are close to each other.
Any help appreciated. thanks Maurizio Mencuccini school of Geosciences University of Edinburgh (UK) -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.
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