Abstract

In the taxonomic congruence approach to systematics, data sets are analyzed separately, and corroboration among data sets is indicated by replicated components in topologies derived from the separate analyses. By contrast, in the total evidence and conditional combination approaches, characters from different data sets are mixed in combined phylogenetic analyses. In optimal topologies derived from such simultaneous analyses, support for a particular node may be attributed to one, some, or all of the individual data sets. Partitioned branch support (PBS) is one technique for describing the distribution of character support and conflict among data sets in simultaneous analysis. PBS is analogous to branch support (BS), but recognizes hidden support and conflicts that emerge with the combination of characters from different data sets. For both BS and PBS, support for a particular node is interpreted as the difference in cost between optimal and suboptimal topologies. A different measure, the clade stability index (CSI), assesses the robustness of a particular node through the successive removal of characters. Here, we introduce variations of the CSI, the data set removal index (DRI) and nodal data set influence (NDI), that indicate the stability of a particular node to the removal of entire data sets. Like PBS, the DRI and NDI summarize the influence of different data sets in simultaneous analysis. However, because these new methods and PBS use different perturbations to assess stability, DRI and NDI scores do not always predict PBS scores and vice versa. In this report, the DRI and NDI are compared to PBS and taxonomic congruence in a cladistic analysis of 17 data sets for Artiodactyla (Mammalia). Five indices of hidden support and conflict are defined and applied to the combined artiodactyl character set. These measures identify substantial hidden support for controversial relationships within Artiodactyla. Hidden character support is ignored in the taxonomic congruence approach to systematics, but the DRI, NDI, and PBS utilize this cryptic information in estimates of support among data sets for a given node.

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