Dissimilarity indices differ in the relative weight given to rare species. Heavy-weighting of rare species may be justified in terms of sampling. An index may erroneously estimate high dissimilarity between two identical communities if they are composed of many rare species and the sampling effort is insufficient to observe most of them in both samples. Heavy-weighting of rare species is thought to compensate for this negative bias. I evaluated two quantitative indices that heavy-weight rare species, NNESS (New Normalized Expected Species Shared) and Goodall, and two probability versions of the Sørensen index, one that takes into account shared unseen rare species and the other that does not. They were compared against the widely used Bray-Curtis (or Sørensen quantitative) and the Morisita-Horn. Indices were computed using raw abundance data or coded data that heavy-weight rare species (frequency in sample units, log-transformation and standardization by the maximum abundance within species). Indices were evaluated for their ability to distinguish, using distance-based MANOVA, season-defined (summer, winter) groups of samples of stream macroinvertebrates and groups of samples obtained by simulation. Sørensen corrected for unseen shared species performed poorly in the empirical study and intermediate in the simulations. NNESS was good in the empirical study and intermediate in the simulations. Goodall scored inversely as NNESS, being intermediate in the empirical assessment and very good in the simulations. The Sørensen uncorrected for unseen shared species, Bray-Curtis and the Morisita-Horn presented poor or intermediate results using raw abundance data. Their performance, however, improved consistently using coded data that heavy-weight rare species and made them good or very good. I conclude that heavy-weighting rare species improves the ability to detect multivariate groups. Heavy-weighting of rare species may be achieved either by using specific formulae (NNESS, Goodall) or using coded data.

相异指数在赋予稀有物种的相对权重上有所不同。重采样稀有物种可能是合理的。如果一个指数由两个稀有物种组成，并且两个样本都无法观察到大部分相同物种，则该指数可能会错误地估计两个相同群落之间的高度相似性。人们认为，对稀有物种进行重加权可以弥补这种负面影响。我评估了两个定量指标，分别是重量级稀有物种NNESS（共享的新归一化预期物种）和Goodall，以及两个Sørensen指数的概率版本，一个考虑了未知稀有物种的共享，而另一个则没有考虑。将它们与广泛使用的Bray-Curtis（或Sørensen定量）和Morisita-Horn进行了比较。使用原始的丰度数据或重量级稀有物种的编码数据（样本中的频率，对数转换和通过物种内最大丰度进行标准化）来计算指数。使用基于距离的MANOVA评估了指数区分河流大型无脊椎动物样本的季节定义（夏季，冬季）组和通过模拟获得的样本的能力。Sørensen纠正了在经验研究中表现不佳而在模拟中处于中间状态的看不见的共有物种的问题。NNESS在实证研究方面表现出色，在模拟方面表现出色。Goodall的得分与NNESS相反，在经验评估中处于中等水平，在模拟方面也非常出色。索伦森（Sørensen）未针对看不见的共有物种进行更正，Bray-Curtis和Morisita-Horn使用原始的丰度数据得出的结果差或中等。但是，使用重磅稀有物种的编码数据后，它们的性能一直得到改善，从而使它们表现良好或非常好。我得出的结论是，对稀有物种进行权重设置可以提高检测多变量组的能力。可以通过使用特定公式（NNESS，Goodall）或使用编码数据来实现对稀有物种的重加权。