Data from: The use of MSR (Minimum Sample Richness) for sample assemblage comparisons

Minimum Sample Richness (MSR) is defined as the smallest number of taxa that must be recorded in a sample to achieve a given level of inter-assemblage classification accuracy. MSR is calculated from known or estimated richness and taxonomic similarity. Here we test MSR for strengths and weaknesses by using 167 published mammalian local faunas from the Paleogene and early Neogene of the Query and Liane area (Massif Central, southwestern France), and then apply MSR to 84 Oligo-Miocene faunas from Riversleigh, northwestern Queensland, Australia. In many cases, MSR is able to detect the assemblages in the data set that are potentially too incomplete to be used in a similarity-based comparative taxonomic analysis. The results show that the use of MSR significantly improves the quality of the clustering of fossil assemblages. We conclude that this method can screen sample assemblages that are not representative of their underlying original living communities. Ultimately, it can be used to identify which assemblages require further sampling before being included in a comparative analysis.

最小样本丰富度（Minimum Sample Richness, MSR）被定义为：为达成既定的组合间分类准确率水平，样本中需记录的最少分类单元数量。MSR的计算基于已知或估算的分类单元丰富度与分类学相似度。本研究采用法国西南部中央高原Query与Liane地区古近纪及新近纪早期的167套已发表的本地哺乳动物化石群，对MSR的优缺点进行检验；随后将MSR应用于澳大利亚昆士兰州西北部里弗斯利（Riversleigh）地区的84套渐新世-中新世化石群数据。在多数情形下，MSR能够识别数据集中完整性不足、无法用于基于相似度的分类学比较分析的化石组合。研究结果表明，采用MSR可显著提升化石组合聚类分析的质量。本研究得出结论：该方法可筛选出无法代表其对应原始现生群落的样本组合。最终，该方法可用于识别哪些化石组合在纳入比较分析前需要进一步补充采样。