Biodiversity Science ›› 2012, Vol. 20 ›› Issue (2): 151-158.doi: 10.3724/SP.J.1003.2012.08163

• Methodologies • Previous Article     Next Article

Point pattern analysis based on different null models for detecting spatial patterns

Xinting Wang1, 2, Yali Hou1, Cunzhu Liang2∗, Wei Wang2, Fang Liu1, 2   

  1. 1School of Energy and Power Engineering, Inner Mongolia University of Technology, Huhhot 010051

    2College of Life Sciences, Inner Mongolia University; Sino-US Center for Conservation, Energy and Sustainability Science in Inner Mongolia (SUCCESS), Huhhot 010021
  • Received:2011-09-15 Revised:2011-11-11 Online:2012-04-09
  • Cunzhu Liang

Understanding spatial distribution patterns has been a central focus of plant ecology since its inception. Spatial patterns of individuals within populations are closely linked to processes; determining these underlying processes remains a major objective of ecological research. Spatial patterns are often determined using a point pattern, a data set consisting of a series of mapped point locations within a study area. The simplest and most widely used null model for analyzing point patterns is the complete spatial randomness (CSR) model. In fact, other null models are rarely used. This paper aims to provide guidance to ecologists when quantifying the underlying processes responsible for spatial patterns of ecological phenomena using point patterns and null models. Photography orientation was used to estimate the point pattern of Leymus chinensis in different restored successional stages in a typical steppe, and complete spatial randomness, Poisson and double-cluster processes were used to analyze spatial patterns of L. chinensis based on this data set. In the early stages of succession, the distribution of L. chinensis fit well with the nested double-cluster process for all scales in the community block of 10 m×10 m. Over time, the distribution fits better with the Thomas process at all scales. This ecological succession phenomenon may be induced by intra-specific competition, but cannot be explained by density interactions. Population territory density could possibly explain the phenomenon. Our study is an important example of successful analysis of population spatial patterns using point patterns and complex null models.

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