Testing association between species abundance and a continuous variable with Kolmogorov-Smirnov statistics
Testing association between species abundance and a continuous variable with Kolmogorov-Smirnov statistics
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DOI：
10.1007/bf00045147
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发表时间：
1996-05-01
期刊：
影响因子：
--
通讯作者：
Henderson, A
中科院分区：
文献类型：
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作者：
Pacheco, MAW;Henderson, A
物种丰富度与一个连续的环境变量之间的联系经常用回归或相关分析来检验。然而，由于这些方法忽略了研究区域内变量水平的范围和频率分布，它们可能会产生误导性的结果。我们举了例子来说明这一论点。要检验物种丰度与一个连续变量之间的联系，一个更好的办法是将研究区域的变量水平与该物种所在地点的变量水平进行比较。如果特定物种丰度与给定的连续变量不相关，则在物种个体出现的地方测量的该变量水平的频率分布应反映在研究区域测量的该变量水平的频率分布。我们解释了如何使用单样本和双样本Kolmogorov-Smirnov统计量来比较个体所在的变量水平与研究区域中的点的累积相对频率。我们讨论了这些测试的统计数据、假设、限制和优势。
Association of species abundance with a continuous environmental variable is frequently tested with regression or correlation analyses. However, because these methods ignore the range and frequency distribution of levels of the variable occurring in the study area, they may generate misleading results. We give examples to illustrate the argument. A better approach to test the association between species abundance and a continuous variable should compare levels of the variable in the study area to levels of the variable occurring in sites occupied by the species. If a particular species abundance is not associated with a given continuous variable, then the frequency distribution of levels of this variable measured where individuals of the species occur should mirror the frequency distribution of levels of the variable measured over the study area. We explain how to use the one- and two-sample Kolmogorov-Smirnov statistics to compare the cumulative relative frequencies of levels of the variable where individuals are present with points in the study area. We discuss the statistics, assumptions, limitations, and advantages of these tests.