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The Chord‐Normalized Expected Species Shared (CNESS)‐distance represents a superior measure of species turnover patterns
Methods in Ecology and Evolution ( IF 6.6 ) Pub Date : 2019-12-13 , DOI: 10.1111/2041-210x.13333
Yi Zou 1 , Jan Christoph Axmacher 2
Affiliation  

  1. Measures of β‐diversity characterizing the difference in species composition between samples are commonly used in ecological studies. Nonetheless, commonly used dissimilarity measures require high sample completeness, or at least similar sample sizes between samples. In contrast, the Chord‐Normalized Expected Species Shared (CNESS) dissimilarity measure calculates the probability of collecting the same set of species in random samples of a standardized size, and hence is not sensitive to completeness or size of compared samples. To date, this index has enjoyed limited use due to difficulties in its calculation and scarcity of studies systematically comparing it with other measures.
  2. Here, we developed a novel R function that enables users to calculate ESS (Expected Species Shared)‐associated measures. We evaluated the performance of the CNESS index based on simulated datasets of known species distribution structure, and compared CNESS with more widespread dissimilarity measures (Bray–Curtis index, Chao–Sørensen index, and proportionality‐based Euclidean distances) for varying sample completeness and sample sizes.
  3. Simulation results indicated that for small sample size (m) values, CNESS chiefly reflects similarities in dominant species, while selecting large m values emphasizes differences in the overall species assemblages. Permutation tests revealed that CNESS has a consistently low CV (coefficient of variation) even where sample completeness varies, while the Chao–Sørensen index has a high CV particularly for low sampling completeness. CNESS distances are also more robust than other indices with regards to undersampling, particularly when chiefly rare species are shared between two assemblages.
  4. Our results emphasize the superiority of CNESS for comparisons of samples diverging in sample completeness and size, which is particularly important in studies of highly mobile and species‐rich taxa where sample completeness is often low. Via changes in the sample size parameter m, CNESS furthermore cannot only provide insights into the similarity of the overall distribution structure of shared species, but also into the differences in dominant and rare species, hence allowing additional, valuable insights beyond the capability of more widespread measures.


中文翻译:

Chord归一化的预期物种共有量(CNESS)距离代表了物种周转模式的优异度量

  1. 表征样品之间物种组成差异的β-多样性度量通常用于生态学研究。但是,常用的相异性度量要求较高的样本完整性,或者样本之间至少具有相似的样本大小。相比之下,Chord归一化的预期物种共有量(CNESS)差异量度计算的是在标准化大小的随机样本中收集同一物种集合的概率,因此对比较样本的完整性或大小不敏感。迄今为止,由于该指数的计算困难以及缺乏系统地将其与其他指标进行比较的研究,因此该指数的使用受到限制。
  2. 在这里,我们开发了一种新颖的R函数,使用户能够计算与ESS(共享的期望物种)相关的度量。我们基于已知物种分布结构的模拟数据集评估了CNESS指数的性能,并将CNESS与更广泛的不相似性度量(Bray–Curtis指数,Chao–Sørensen指数和基于比例的欧几里得距离)进行了比较,以评估不同的样本完整性和样本量大小。
  3. 仿真结果表明,对于较小的样本量(m)值,CNESS主要反映了优势物种的相似性,而选择较大的m值则强调了总体物种组合的差异。置换测试显示,即使样品完整性发生变化,CNESS的CV(变异系数)也始终较低,而Chao-Sørensen指数具有较高的CV,尤其是对于较低的采样完整性。就欠采样而言,CNESS距离也比其他指标更健壮,尤其是当两个集合体之间主要共享稀有物种时。
  4. 我们的结果强调了CNESS在比较样品完整性和大小上不同的样品时的优越性,这在研究样品完整性经常较低的高流动性和物种丰富的分类单元中尤为重要。通过样本大小参数m的变化,CNESS不仅可以洞察共有物种总体分布结构的相似性,而且可以洞悉优势物种和稀有物种的差异,从而提供更多,有价值的见识,而不仅仅是更广泛的能力。措施。
更新日期:2019-12-13
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