Testing of null hypotheses in exploratory community analyses: similarity profiles and biota-environment linkage
Testing of null hypotheses in exploratory community analyses: similarity profiles and biota-environment linkage
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DOI：
10.1016/j.jembe.2008.07.009
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发表时间：
2008-11-15
影响因子：
2
通讯作者：
Gorley, Raymond N.
中科院分区：
文献类型：
--
作者：
Clarke, K. Robert;Somerfield, Paul J.;Gorley, Raymond N.
对“缺乏结构”的零假设的检验在任何探索性研究中都应该发挥重要作用，以防止偶然获得的样本模式的解释，并描述了两种新的这种类型的检验。在群落生态学和许多其他环境背景中出现的多变量分析中，例如在将聚集模式与强迫环境变量联系起来(梯度分析)时，通常可以检查的大量非生物变量的组合加剧了机会关联的问题。描述了一种允许这种选择偏差的检验(全局最佳检验)，该检验适用于任何不同的测量，仅利用等级不同，并且通过排列操作，假定生物到非生物的链接没有特定的分布形式或参数表达。第二个置换过程，相似性简档例程(SIMPROF)，测试在先验的非结构化样本集中是否存在样本组(或更多连续的样本模式)，对于先验的非结构化样本集合！结构化测试(例如，广泛使用的ANOSIM)无效。一个背景是从层次聚类分析中解释树状图：一系列SIMPROF测试为更精细地细分亚组提供了客观的停止规则。将这两个检验联系在一起的是第三个方法论链，将de‘ath的多变量等价物单变量CART分析(分类和回归树)适应于非参数上下文。这产生了基于它们的组装数据的样本的分裂的、受约束的、分层的聚类分析，称为链接树。约束是，树的每个二进制划分对应于环境变量之一上的阈值，并且与相关的非参数例程一致，最大化由ANOSIM R统计量测量的两组的高维分离。因此，这种连锁树为样本的每个生物细分提供了非生物“解释”，但是，与无约束聚类一样，LINKTREE例程需要客观的停止规则以避免过度解释，这些也是由SIMPROF测试序列提供的。来自海洋生态学文献的数据说明了这三个新发展的相互联系。(C)2008爱思唯尔B.V.保留所有权利。
Tests for null hypotheses of 'absence of structure' should play an important role in any exploratory study, to guard against interpretation of sample patterns that could have been obtained by chance, and two new tests of this type are described. In the multivariate analyses that arise in community ecology and many other environmental contexts, e.g. in linking assemblage patterns to forcing environmental variables (gradient analysis), the problem of chance associations is exacerbated by the large number of combinations of abiotic variables that can usually be examined. A test which allows for this selection bias is described (the global BEST test), which applies to any dissimilarity measure, utilises only rank dissimilarities, and operates by permutation, assuming no specific distributional form or parametric expression for the biotic to abiotic links. A second permutation procedure, the similarity profile routine (SIMPROF), tests for the presence of sample groups (or more continuous sample patterns) in a priori unstructured sets of samples, for which an a prior! structured test (e.g. the widely-used ANOSIM) is invalid. One context is in interpreting dendrograms from hierarchical cluster analyses: a series of SIMPROF tests provides objective stopping rules for ever-finer dissection into subgroups. Connecting these two tests is a third methodological strand, adapting De'ath's multivariate equivalent of univariate CART analysis (Classification And Regression Trees) to a non-parametric context. This produces a divisive, constrained, hierarchical cluster analysis of samples, based on their assemblage data, termed a linkage tree. The constraint is that each binary division of the tree corresponds to a threshold on one of the environmental variables and, consistently with related non-parametric routines, maximises the high-dimensional separation of the two groups, as measured by the ANOSIM R statistic. Such linkage trees therefore provide abiotic 'explanations' for each biotic subdivision of the samples but, as with unconstrained clustering, the LINKTREE routine requires objective stopping rules to avoid over-interpretation, these again being provided by a sequence of SIMPROF tests. The inter-connectedness of these three new developments is illustrated by data from the literature of marine ecology. (C) 2008 Elsevier B.V. All rights reserved.