生物多样性 ›› 2012, Vol. 20 ›› Issue (2): 151-158.DOI: 10.3724/SP.J.1003.2012.08163

• 方法 • 上一篇    下一篇

基于不同零模型的点格局分析

王鑫厅1,2, 侯亚丽1, 梁存柱2,*(), 王炜2, 刘芳1,2   

  1. 1 内蒙古工业大学能源与动力工程学院, 呼和浩特 010051
    2 内蒙古大学生命科学学院, 中美生态、能源及可持续性科学内蒙古研究中心, 呼和浩特 010021
  • 收稿日期:2011-09-15 接受日期:2011-11-21 出版日期:2012-03-20 发布日期:2012-04-09
  • 通讯作者: 梁存柱
  • 作者简介:* E-mail: bilcz@imu.edu.cn
  • 基金资助:
    国家重点基础研究发展计划(973计划)(2010CB950602);内蒙古自治区重大科技项目(20091403);内蒙古自治区重大科技项目(20101405);内蒙古自然科学基金(2011MS0517)

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. 1 School of Energy and Power Engineering, Inner Mongolia University of Technology, Huhhot 010051
    2 College 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 Accepted:2011-11-21 Online:2012-03-20 Published:2012-04-09
  • Contact: Cunzhu Liang

摘要:

在种群空间格局研究中, 定量分析格局及其形成过程已成为生态学家的主要目标。在量化分析的众多方法中, 点格局分析是最常用的方法, 而在选择零模型时, 完全空间随机模型以外的复杂零模型很少使用, 实际上, 这些零模型可能有助于认识格局的内在特征。为此, 我们在研究实例中, 选择完全空间随机模型(complete spatial randomness)、泊松聚块模型(Poisson cluster process)和嵌套双聚块模型(nested double-cluster process)对典型草原处于不同恢复演替阶段的羊草(Leymus chinensis)种群空间格局进行了分析。结果发现: 完全空间随机模型仅能检测种群在不同尺度下的格局类型; 而通过泊松聚块模型和嵌套双聚块模型检验表明, 在恢复演替的初期阶段, 羊草种群在小尺度范围内偏离泊松聚块模型, 而在整个取样范围内完全符合嵌套双聚块模型; 随着恢复演替时间的推移, 在恢复演替的后期, 在整个取样尺度上, 羊草种群与泊松聚块模型相吻合。这是很有意义的生态学现象。这一实例表明在应用点格局分析种群空间格局时, 仅通过完全空间随机模型的检验来分析格局特征, 或许很难论证复杂的生态过程, 而选择一些完全空间随机模型以外的较复杂的零模型, 可能发现一些有价值的生态学现象, 对揭示格局掩盖下的内在机制有所裨益。

关键词: 完全空间随机模型, 泊松聚块模型, 嵌套双聚块模型, Leymus chinensis, 恢复演替, 摄影定位法, 种群领地密度

Abstract

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.

Key words: complete spatial randomness, Poisson cluster process, nested double-cluster process, Leymus chinensis, restoring succession, photography orientation, territory density