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：为达成指定水平的化石组合间分类准确率，单一样本中需记录的最少类群数量。MSR的计算依托已知或估算的类群丰富度与分类学相似度。本研究以法国西南部中央高原奎里与利安地区古近纪和新近纪早期的167份已发表的地方性哺乳动物群化石数据为研究材料，检验MSR的优势与局限性；随后将该方法应用于澳大利亚昆士兰州西北部里弗斯利地区的84份渐新世-中新世化石群数据。在多数案例中，MSR可识别出数据集中那些因过于残缺而无法适用于基于相似度的比较分类学分析的化石组合。研究结果显示，采用MSR方法可显著提升化石组合聚类分析的质量。本研究最终得出结论：该方法能够筛选无法代表其对应原始现生群落的样本组合，最终可用于甄别哪些化石组合在纳入比较分析前需要进一步采样补充。