The spatial pattern of plant population is one of primary issues in ecological research. Point pattern analy-sis is considered as an important method to study the spatial pattern of plant population. Ripley's K function has been commonly used for point pattern analysis. However, the cumulative effect of Ripley's K function may lead to specific spatial pattern charcteristics. To explore how the cumulative effect of Ripley's K function affects population pattern, the data of clumped distribution, random distribution and regular distribution of Stipa grandis were simulated by R software. All data generated by R software were analyzed by Ripley's K function and the non-cumulative pairwise correlation function g(r). The results showed that for clumped distribution (or regular distribution), the cumulative effect of Ripley's K function was manifested in two aspects. On the one hand, the scale of clumped distribution (or regular distribution) was increased due to Ripley's K function. On the other hand, Ripley's K function could detect the difference of the distribution of cluster (or negative interaction range) in the sampling space, exhibiting different pattern characteristics. For random distribution, Ripley's K function had no cumulative effect. In conclusion, the combination of Ripley's K function and pairwise correlation function by collecting replicate samples could better reveal the essential characteristics of the pattern in the study of population pattern.
植物种群空间格局是生态学研究的基本问题之一, 点格局分析已成为植物种群空间格局研究的重要方法之一, 其中, Ripley's K函数是点格局常用分析方法。然而, 由于Ripley's K函数具有累积效应, 这种累积效应可能会导致特定的格局特征。为了探讨Ripley's K函数的累积效应如何影响种群格局研究结果, 以大针茅种群数据为基础, 通过R软件模拟聚集分布、随机分布和均匀分布3种格局类型, 对比使用具有累积效应的Ripley's K函数和不具有累积效应的成对相关函数进行分析。结果表明: 对于聚集分布和均匀分布, Ripley's K函数的累积效应表现在2个方面: 一方面增加了聚集分布(或均匀分布)的尺度, 另一方面能够检测到聚块或负相互作用范围在取样空间的分布差异而表现出不同的格局特征。而对于随机分布, Ripley's K函数没有累积效应。因此, 在种群格局研究过程中, 通过重复取样且Ripley's K 函数与成对相关函数相结合的方法探讨种群格局, 更能揭示空间格局的本质特征。.
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