A new family of dissimilarity metrics for discrete character matrices that include inapplicable characters and its importance for disparity studies

The use of discrete character data for disparity analyses has become more popular, partially due to the recognition that character data describe variation at large taxonomic scales, as well as the increasing availability of both character matrices co-opted from phylogenetic analysis and software tools. As taxonomic scope increases, the need to describe variation leads to some characters that may describe traits not found across all the taxa. In such situations, it is common practice to treat inapplicable characters as missing data when calculating dissimilarity matrices for disparity studies. For commonly used dissimilarity metrics like Will’s GED and Gower’s coefficient, this can lead to the re-ranking of pairwise dissimilarities, resulting in taxa that share more primary character states being assigned larger dissimilarity values than taxa that share fewer. We introduce a family of metrics that proportionally weight primary characters according to the secondary characters that descr...

离散性状数据（discrete character data）在差异度分析（disparity analyses）中的应用愈发普及，这一方面源于学界认识到性状数据可在大分类学尺度上描述变异，另一方面则得益于从系统发育分析（phylogenetic analysis）中借用的性状矩阵与相关软件工具的可获取性不断提升。随着分类学范围的扩大，为描述变异而纳入的部分性状，其对应的特征可能并非在所有类群中均存在。在此类情形下，在为差异度研究计算相异矩阵（dissimilarity matrices）时，学界通常将不适用性状（inapplicable characters）视为缺失数据（missing data）进行处理。对于威尔广义欧氏距离（Will's GED）、高沃系数（Gower's coefficient）等常用相异度指标，该处理方式会导致成对相异度（pairwise dissimilarities）的重新排序，使得共享更多初级性状状态（primary character states）的类群，反而被赋予比共享更少初级性状状态的类群更高的相异度数值。我们提出了一类度量指标，其可依据用于描述[原文未完整收尾]的次级性状，对初级性状按比例进行加权。