Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis
Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis
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DOI：
10.2307/1938672
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发表时间：
1986-10
期刊：
影响因子：
4.8
通讯作者：
C. Braak
中科院分区：
文献类型：
--
作者：
C. Braak
本文介绍了一种新的多元分析技术，该技术旨在将群落组成与已知的环境变化相关联。这种技术是对应分析（互反平均）的一种延伸，对应分析是一种常用的排序技术，它从物种出现或丰度数据中提取连续的变异轴。这类排序轴通常借助有关环境变量的外部知识和数据来解释；这种两步法（先排序，然后识别环境梯度）被称为间接梯度分析。在这种被称为典范对应分析的新技术中，根据已知的环境变量来选择排序轴，通过施加额外的限制，即轴是环境变量的线性组合。通过这种方式，群落变异可以直接与环境变异相关联。环境变量可以是定量的，也可以是定性的。能提取的轴的数量与环境变量的数量相同。可以在该技术中纳入去趋势方法以消除弓形效应。当物种相对于环境梯度具有钟形响应曲线或曲面时，（去趋势的）典范对应分析是一种有效的排序技术，因此，与典范相关分析相比，它更适合用于分析群落组成和环境变量的数据。这种新技术产生一种排序图，其中点代表物种和样地，向量代表环境变量。这样的图展示了群落组成的变异模式，这些模式可以由环境变量得到最好的解释，并且还大致呈现了物种沿着每个环境变量分布的“中心”。对于狩猎蜘蛛、堤坝植被以及沿污染梯度的藻类数据集，这类图有效地总结了群落与环境之间的关系。
A new multivariate analysis technique, developed to relate community composition to known variation in the environment, is described. The technique is an extension of correspondence analysis (reciprocal averaging), a popular ordination technique that extracts continuous axes of variation from species occurrence or abundance data. Such ordination axes are typically interpreted with the help of external knowledge and data on environmental variables; this two—step approach (ordination followed by environmental gradient identification) is termed indirect gradient analysis. In the new technique, called canonical correspondence analysis, ordination axes are chosen in the light of known environmental variables by imposing the extra restriction that the axes be linear combinations of environmental variables. In this way community variation can be directly related to environmental variation. The environmental variables may be quantitative or nominal. As many axes can be extracted as there are environmental variables. The method of detrending can be incorporated in the technique to remove arch effects. (Detrended) canonical correspondence analysis is an efficient ordination technique when species have bell—shaped response curves or surfaces with respect to environmental gradients, and is therefore more appropriate for analyzing data on community composition and environmental variables than canonical correlation analysis. The new technique leads to an ordination diagram in which points represent species and sites, and vectors represent environmental variables. Such a diagram shows the patterns of variation in community composition that can be explained best by the environmental variables and also visualizes approximately the "centers" of the species distributions along each of the environmental variables. Such diagrams effectively summarized relationships between community and environment for data sets on hunting spiders, dyke vegetation, and algae along a pollution gradient.