Character distribution maps: a visualization method for comparative cladistics

AbstractWhitlock, J.A. and Wilson, J.A. 2013. Character distribution maps: avisualization method for comparative cladistics. — Acta Zoologica (Stockholm)94: 490–499.The rise of computational methods and widespread availability of phylogeneticsoftware packages has resulted in a dramatic increase in the number of phyloge-netic analyses published each year. We anticipate that as the number of workersand the number of analyses continues to increase, interest in so-called ‘meta-analyses’ that compare not just the resultant trees, but the data themselves, willalso increase. Here, we demonstrate a simple method for authors to facilitatesuch comparisons by visually representing the character data included in theiranalyses, the Character Distribution Map (CDM). John A. Whitlock, Life Sciences Institute, University of British Columbia, 2350Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.E-mail: jawhitl@dentistry.ubc. ca.IntroductionCharacter data in phylogenetic analyses are sometimesdescribedasbeingtrappedina‘blackbox’,hiddenfromout-side observers (Patterson and Johnson 1997; Rieppel andKearney 2007; Sereno 2009). Although the data themselvesare explicitly given in an alphanumeric character –taxonmatrix, what is actually ‘hidden’ are the assumptions implicitin the selection of those data, which include criteria for char-acter inclusion and exclusion, search images for particularstates, partitioning of variation into states, as well as construc-tion of character state networks/trees and the order, directionand polarity of states (Jenner 2001, 2004). This can result in aconsiderable number of hidden assumptions, because even asmall analysis contains numerous independent anatomicalobservations (e.g. 10 taxa 9 40 characters = 400 observa-tions). Presentation of data in character–taxon matrices can makeit difﬁcult to compare support for competing tree topologies.When fundamental differences between topologies exist (e.g.the position of turtles within Sauropsida), it is essential to beable to easily root out the underlying differences in data selec-tion in order to evaluate the results of each. Although methodsexist for comparing robustness of topologies (e.g. decay indi-ces and bootstrap values) and goodness-of-ﬁt of alternativetopologies to a single matrix (i.e. Templeton tests), they donot allow for comparisons between the kinds of data that actu-ally comprise the matrices themselves.Continued emphasis on phylogenetic context, along withthe convenience of computer-assisted cladistics (Grant et al.2003), will continue to increase the number of phylogenetichypotheses. For example, approximately, 79 phylogeneticanalyses of Mesozoic dinosaurs have been published since2009 (Appendix 1). Of these, nearly 25% (19 analyses) iden-tiﬁed signiﬁcant topological differences between their analysisand those of other workers (Appendix 1). Few of these anal-yses attempt to identify the source of those differences(Sereno and Brusatte 2009; Wilson and Upchurch 2009;Ksepka and Norell 2010; Agnolin and Novas 2011; Whitlock2011; Whitlock et al. 2011; D’Emic 2012; Mannion et al.2012). Although ‘comparative cladistic’ metrics such as the char-acter-similarity index (CSI) and character state similarityindex (CSSI) are capable of providing a quantitative assess-ment of similarity between analyses (Sereno 2009), they arelabour-intensive exercises and must be recalculated for everycomparison a researcher wishes to make.We describe a simple method by which workers can obtaina birds-eye view of character data, from which patterns impli-cit to character selection are more easily recognized. Thismethod will prove useful for exploratory investigation intophylogenetic datasets, or for more detailed comparative