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Hipp1,* and Marcial Escudero2 1Herbarium, The Morton Arboretum, 4100 Illinois Route 53, Lisle IL 605321293, USA 2 Department of Molecular Biology and Biochemical Engineering, Pablo de Olavide University, Ctra. Utrera km 1, 41013 Sevilla, Spain Received on XXXXX; revised on XXXXX; accepted on XXXXX Associate Editor: XXXXXXX *abstract Summary: MATICCE is a new software package written in the R language that implements an information-theoretic approach to estimating the probability of continuous character transitions on phylogenetic trees. It automates batch-analysis of alternative models of character change utilizing the OUCH (R) package; estimates the relative support for character changes having occurred at numerous nodes on a phylogeny while integrating over uncertainty in the phylogeny and the model of character change; and provides discrete-time simulation functions for visualizing analysis results as well as a priori models. Availability: MATICCE is open source and written entirely in R. Source and compiled binaries are freely available through R-Forge ( HYPERLINK "http://r-forge.r-project.org/" http://r-forge.r-project.org/ ). Contact: HYPERLINK "mailto:ahipp@mortonarb.org" ahipp@mortonarb.org introduction Inferring the evolutionary history of species traits is a fundamental problem in evolutionary biology and an issue of practical concern for ecologists, developmental biologists, and others testing biological hypotheses using phylogenetic data. Methods of analyzing categorical data in a phylogenetic context have addressed the problem of localizing character transitions to branches {Huelsenbeck, 2003 #533;Ree, 2005 #1286;Nielsen, 2002 #535}. New Numerous methods utilizing phylogenetic generalized least squares model shifts in the dynamics of continuous character evolution {Revell, 2009 #1444;O'Meara, 2006 #813;Scales, 2009 #1469;Butler, 2004 #598;Pagel, 1999 #432;Blomberg, 2003 #1312} based on well-justified assumptions about the variance-covariance structure of comparative continuous data under alternative evolutionary scenarios {Butler, 2004 #598;Housworth, 2004 #930;Hansen, 1996 #933;Martins, 1997 #421;Hansen, 2008 #1277}. XXX However, there is no standardized method for scanning a phylogenetic tree and identifying where on the tree significant changes in continuous character distribution have occurred. have received less focus, despite the fact that among-clade differences in character distributions often reflect a biologically interesting process (shifts in environment, changes in evolutionary dynamics or population-level processes, etc.). Alternatives to the constant variance model (Brownian Motion model) of continuous character evolution have until recently focused primarily on branch-length transformations that model how different evolutionary scenarios affect phylogenetic structure and the mode and tempo of evolution {Pagel, 1999 #432;Blomberg, 2003 #1312;O'Meara, 2006 #813}. These methods, however, are not generally useful for identifying (1) whether an apparent change in the distribution of a character on a tree is significant enough to warrant further investigation, (2) whether the particular distribution tells us something about the evolution of this trait, and (3) where on the phylogeny this change in distribution took place. Evaluating alternative Ornstein-Uhlenbeck (O-U) models in a phylogenetic context {Butler, 2004 #598;Martins, 1997 #421;Hansen, 2008 #1277} may be used to address such questions about evolutionary transitions in continuous characters. The use of O-U models typically focuses on testing adaptive scenarios {Bergmann, 2009 #1319;Smith, 2008 #1091;Verdu, 2006 #787} or phylogenetic constraints {Diniz-Filho, 2001 #987;Blomberg, 2003 #1312}. However, tTwo recentwo recent studiesies {Hipp, 2007 #1051;Lumbsch, 2008 #1282} have utilizedhave utilized an information- theoretic approach to identifying where on a phylogeny there have been shifts in the stationary distribution of a continuous character. The method utilizes the Ornstein-Uhlenbeck (O-U) model of character evolution as implemented in ouch [CITATION], modeling character transitions as shifts in character equilibrium. A character evolving according to an O-U process evolves stochastically toward an equilibrium distribution with mean ¼ a t r a t e ¸ ( x ¼ ) [ C L A R I F Y T H I S ] . T h e u s e o f O - U m o d e l s t y p i c a l l y f o c u s e s o n t e s t i n g a d a p t i v e s c e n a r i o s { B e r g m a n n , 2 0 0 9 # 1 3 1 9 ; S m i t h , 2 0 0 8 # 1 0 9 1 ; V e r d u , 2 0 0 6 # 7 8 7 } o r p h y l o g e n e t i c c o n s t r a i n t s { D i n i z F i l h o , 2 0 0 1 # 9 8 7 ; B l o m b e r g , 2 0 0 3 # 1 3 1 2 } , w i t h t h e l o c a t i o n s of continuous character transitions defined a priori with respect to shifts in hypothesized selective regimes. O-U models to estimate the probability of continuous character transitions on phylogenetic trees, modeled as shifts in character equilibrium or optimum. The approach taken introduced in Hipp 2007 entailsed (1) identifying n nodes at which a change may have occurred, (2) evaluating support for all 2n models that allow all permutations of change at those nodes, and (3) using a model-averaging approach to estimateing the support for a transition in character state at each node studied as the cumulative information criterion weight for all models entailing a shift at that node {Burnham, 2002 #791}. The method is reminiscent of stepwise AIC model-selection {Yamashita, 2007 #1532;Alfaro, 2009 #1509}. However, by taking a model-averaging approach rather than a model-selection approach, it is not susceptible to the significance threshold effects that introduce uncertainty into modelselection. The software described in this application notepaper— MAaTIiCCE, a package written in the R statistical language {RDevelopment-Core-Team, 2004 #588}—extends this the method by automatically generating the potentially large number of model specifications required to test character transition hypotheses and providing a set of tools for flexibly defining nodes for analysis; performing and summarizing analyses over sets of trees, integrating over phylogenetic uncertainty and allowing analyses to be performed even for nodes that are not found in all trees in a set; simulating and visualizing character evolution under a user-specified model or using model-averaged parameters from a MATICCE analysis; and providing an easy interface by which to test numerous whole-tree and partitioned-tree models for a particular phylogenetic branchallowing for phylogenetic uncertainty and for limits on the maximum number of nodes at which transitions are allowed to occur; implementing a set of discrete-time simulations for visualization of character evolution under different models; providing an easy interface by which to test numerous whole-tree and partitioned-tree models for a particular phylogenetic branch; and allowing for flexibility in defining nodes for analysis.. The method is similar to a stepwise AIC approach {Yamashita, 2007 #1532;Alfaro, 2009 #1509}. However, by taking a model-averaging approach rather than a model-selection approach, it is not susceptible to the significance threshold effects that introduce uncertainty into model-selection. description MATICCE utilizes The MaTiCCE package (http://r-forge.rproject.org/projects/mattice/) provides functions for generating the potentially large number of model specifications required to test character transition hypotheses; performing and summarizing analyses over a single tree or a set of trees, allowing analyses to be performed even for nodes that are not found in all trees being studied; simulating and visualizing character evolution under a user-specified model or using model-averaged parameters from a MaTiCCE analysis; and evaluating the relative support for several different character-transition models on a single node. The approach presupposes that (1) flat priors on all models are reasonable; (2) all nodes of interest have been specified; (3) XXX. tThe likelihood calculationfunctions s are all performed in implemented in ouch {Butler, 2004 #598;King, 2009 #1313}, but addressing these particular questions entails summarizing analyses over a large number of specified models and extracting tree data to analyze over phylogenetic uncertainty, functions that would be superfluous in ouch. For more details see the vignette: vignette(‘maticce’). For installlation, install.packages("maticce", repos="http://R-Forge.Rproject.org"). 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A.L.H.’s work was funded by a NESCent visiting scholar award (NSF #EF-0423641) and NSF DEB #0743157. M.E. was supported by XXX. References *To whom correspondence should be addressed. K.Takahashi et al. STYLEREF "Article title" \* MERGEFORMAT style in document. PAGE 2 PAGE 3 Error! 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