On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray-Curtis coefficient for denuded assemblages
On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray-Curtis coefficient for denuded assemblages
复制标题
DOI：
10.1016/j.jembe.2005.12.017
复制
发表时间：
2006-03-07
影响因子：
2
通讯作者：
Chapman, MG
中科院分区：
文献类型：
--
作者：
Clarke, KR;Somerfield, PJ;Chapman, MG
Bray-Curtis相似性被广泛应用于组合数据的多变量分析中，这是基于合理的生物学原因。本文讨论了两个问题，然而，它的实际应用：它的行为是不稳定的（甚至是未定义的）的消失稀疏的样本，可能会被发现作为一个终点，一个严重的影响梯度，或在殖民化研究的起点;和，在共同的物种水平的数据与所有相似性措施，它是敏感的不一致的分类鉴定通过时间。结果表明，后者的问题得到改善的应用程序的“分类相异度”系数，分类独特性指数的概念的自然延伸。两个先前的建议用于存在/不存在数据，在这里表示为Gamma（+）和Theta（+），注意到分别是Bray-Curtis和Kulczynski测量的简单概括。也可以看到他们的能力，pennit排序的组合从广泛的地理尺度，没有共同的物种，并为Bray-Curtis将返回零相似性的所有对的样品。如果可以令人信服地认为，由于相同的原因，而不是由于样本量不足（丝束长度、芯径、样带或样方大小等）而随机发生，对Bray-Curtis系数的形式的简单调整可以产生有意义的MDS显示，否则该MDS显示将崩溃，并且可以改善ANOSIM R统计的值（增加多变量空间中的组的分离）。它也被证明没有任何影响，在所有的Bray-Curtis分析的正常运作时，至少有一个适度的数据是目前为所有samples.Examination的属性，这个“零调整”的Bray-Curtis措施去手牵手与更广泛的讨论竞争相似性，距离或相异性系数（统称：相似性措施）在社区生态学的功效。内在的生物学准则的“布雷-柯蒂斯家庭”的措施（包括Kulczynski，Sorenson，Ochiai和堪培拉相异）是明确的。这些和其他常用的测量方法（如欧几里德、曼哈顿、高尔和卡方距离）是针对撞击事件或空间和时间梯度的几个“经典”数据集计算的。特定系数的行为是判断对由此产生的排序图的可解释性和一个客观的衡量区分先验定义的假设，代表影响条件的能力。一组相似系数的第二阶段MDS图，基于各自生成的多变量模式的相似性（MDS图的MDS，实际上），被认为是有用的，在确定哪些系数从相同的集合矩阵中提取本质上不同的信息。这表明了一种机制，实际分类的过多的相似性措施在文献中定义。基于相似性的ANOSIM R统计和斯皮尔曼rho相关性，其非参数结构使它们在不同的相似性度量中具有绝对可比性，回答了一些系数提取的不同信息是否更多或更少有助于最终生物学解释的问题。(C)2006 Elsevier B.V保留所有权利。
Bray-Curtis similarity is widely employed in multivariate analysis of assemblage data, for sound biological reasons. This paper discusses two problems, however, with its practical application: its behaviour is erratic (or even undefined) for the vanishingly sparse samples that may be found as an end-point to a severe impact gradient, or a start-point in colonisation studies; and, in common with all similarity measures on species-level data, it is sensitive to inconsistency of taxonomic identification through time. It is shown that the latter problem is ameliorated by application of 'taxonomic dissimilarity' coefficients, a natural extension of the concept of taxonomic distinctness indices. Two previous suggestions for use with presence/absence data, denoted here by Gamma(+) and Theta(+), are noted to be simple generalisations of the Bray-Curtis and Kulczynski measures, respectively. Also seen is their ability to pen-nit ordinations of assemblages from wide geographic scales, with no species in common, and for which Bray-Curtis would return zero similarity for all pairs of samples.The primary problem addressed, however, is that of denuded or entirely blank samples. Where it can be convincingly argued that impoverished samples are near-blank from the same cause, rather than by random occurrences from inadequate sample sizes (tow length, core diameter, transect or quadrat size etc.), a simple adjustment to the form of the Bray-Curtis coefficient can generate meaningful MDS displays which would otherwise collapse, and can improve values of the ANOSIM R statistic (increased separation of groups in multivariate space). It is also shown to have no effect at all on the normal functioning of a Bray-Curtis analysis when at least a modest amount of data is present for all samples.Examination of the properties of this 'zero-adjusted' Bray-Curtis measure goes hand-in-hand with a wider discussion of the efficacy of competing similarity, distance or dissimilarity coefficients (collectively: resemblance measures) in community ecology. The inherent biological guidelines underlying the 'Bray-Curtis family' of measures (including Kulczynski, Sorenson, Ochiai and Canberra dissimilarity) are made explicit. These and other commonly employed measures (e.g. Euclidean, Manhattan, Gower and chi-squared distances) are calculated for several 'classic' data sets of impact events or gradients in space and time. Behaviour of particular coefficients is judged against the interpretability of the resulting ordination plots and an objective measure of the ability to discriminate between a priori defined hypotheses, representing impact conditions. A second-stage MDS plot of a set of resemblance coefficients, based on the respective similarities of the multivariate patterns each generates (an MDS of MDS plots, in effect), is seen to be useful in determining which coefficients are extracting essentially different information from the same assemblage matrix. This suggests a mechanism for practical classification of the plethora of resemblance measures defined in the literature. Similarity-based ANOSIM R statistics and Spearman rho correlations, whose non-parametric structure make them absolutely comparable across different resemblance measures, answer questions about whether the different information extracted by some coefficients is more, or less, helpful to the final biological interpretation. (C) 2006 Elsevier B.V All rights reserved.