Efficient synthetic generation of ecological data with preset spatial association of individuals
Canadian Journal of Forest Research, 2021
Many experiments cannot feasibly be conducted as factorials. Simulations using synthetically gene... more Many experiments cannot feasibly be conducted as factorials. Simulations using synthetically generated data are viable alternatives to such factorial experiments. The main objective of the present research is to develop a methodology and platform to synthetically generate spatially explicit forest ecosystems represented by points with a predefined spatial pattern. Using algorithms with polynomial complexity and parameters that control the number of clusters, the degree of clusterization, and the proportion of nonrandom trees, we show that spatially explicit forest ecosystems can be generated time efficiently, which enables large factorial simulations. The proposed method was tested on 1200 synthetically generated forest stands, each of 25 ha, using 10 spatial indices: Clark–Evans aggregation index; Ripley’s K; Besag’s L; Morisita’s dispersion index; Greig–Smith index; the size dominance index of Hui; index of nonrandomness of Pielou; directional index and mean directional index of C...
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Papers by Mihaela Păun