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Supporting Information Fitzpatrick et al. 10.1073/pnas.0809990106 SI Methods likely to lead to 2 and 3, which in turn leads to 4. We can assess Testing the Directionality of Evolutionary Transitions. BayesDiscrete these possible evolutionary transitions by examining the tran- (www.evolution.rdg.ac.uk) (1) uses discrete binary data (0 and 1) sition parameters leading away from value 1. For example, the to assess all possible transitions between 2 traits. Traits assigned as evolutionary pathway of value 1 to 2 to 4 indicates that changes binary variables create 4 possible values (1 value per species) that in trait 2 preceded changes in trait 1, because the value 1 traits can be described as [trait 1,trait 2] as follows: [0,0], [0,1], [1,0], [1,1]. [0,0] shifted to value 2 traits [0,1]. Applying this logic, we can BayesDiscrete allows forward and reverse transitions between these assess the other possible evolutionary transitions described in 4 values, so there are 8 possible transitions between values: 4 our example above. forward and 4 reverse. Each transition parameter (q) is assigned a We assessed the frequency that transition parameters where unique numerical code describing the direction of change between set equal to zero using a reversible-jump Markov chain Monte Carlo (RJ MCMC) method, which explores the entire parameter 2 states. For example, q represents a transition from value 1 to 1,2 space. We selected a subsample of trees from the 2 million value 2, and q represents the reverse transition from state 2 to 2,1 generations produced from our MrBayes analysis. After a state 1. BayesDiscrete assumes that transitions involving simulta- burn-in of 1 million generations, we sampled 500 trees at neous change in 2 traits do not occur (i.e., transitions from [0,0] to intervals of 20,000 generations. This analysis controls for phy- [1,1] or from [0,1] to [1,0]). The 4 trait values and 8 transition logenetic uncertainty by assessing transitions among a broad parameters are illustrated in Scheme 1 subset of trees generated from our MrBayes analysis. We ran the RJ MCMC chain for 5,050,000 iterations, with a burn-in of 50,000 iterations. The chain was sampled every 100th iteration, creating a posterior distribution of 50,000 sample points, from which we determined the mean Ϯ SE transition parameter value, and the proportion of samples where the transition parameter was assigned to zero. Transition parameters less likely to have occurred are frequently assigned to zero, whereas those that are likely are assigned non-zero values. The strength of each tran- sition parameter was also assessed by using the average value of each transition parameter. In general, transitions are considered likely when Z Ͻ 0.10, meaning that Ͻ10% of the iterations from the MC are assigned to zero (1). However, following refs. 2, 3, we also used a more conservative approach to assess trait evolution, which accounts for nonsignificance that may result from low statistical power due to small phylogenies (4). In this approach, we considered Evolutionary pathways from ancestral to derived values can nonsignificant (Z Ͼ 0.10) transitions to be likely evolutionary be determined by assessing the probability of a certain tran- transitions if the nonsignificant transition parameter (q value) sition parameter having occurred relative to another. For was higher than that of the lowest parameter that yielded a example, if value 1 was the ancestral trait and value 4 was the significant transition. 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