A new non-parametric method for analyzing replicated point patterns in ecology

Most ecological studies that involve point pattern analyses are based on a single plot, which prevent the separation of the effects of various processes that could act simultaneously, as well as limiting the conclusions that can be extracted from these studies. However, considering the spatial distribution of individuals in several plots as replicates of the same process could help to differentiate its specific effects from those of other confounding processes. Thus, we introduce a new method for analyzing spatial point patterns that are replicated according to a two–factorial design. By summarizing the spatial patterns as K–functions, the proposed method computes the average K–functions for each level of the two factors (i.e., predictors) and for each combination of levels, before estimating the sum of squared deviations from the overall mean K–function. Inferences of the strength of the relationship between the predictors, their interaction, and the spatial structure are made based on...

绝大多数开展空间点格局分析（point pattern analyses）的生态学研究均基于单一样地，这使得研究者无法分离同时起作用的多种生态过程的独立效应，同时也限制了此类研究可推导出的结论范围。然而，若将多个样地中个体的空间分布视作同一生态过程的重复样方，则有助于区分该过程的特有效应与其他混杂过程的效应。为此，我们提出一种全新的空间点格局分析方法，该方法可针对遵循两因素设计（two–factorial design）的重复样地数据开展分析。该方法先将空间格局归纳为K函数（K–functions），分别计算两个因素（即预测变量）各水平及其所有水平组合下的平均K函数，随后估算其与总平均K函数的平方偏差之和。针对预测变量、其交互作用与空间结构之间关联强度的统计推断，将基于……展开。