In practice, the location of the root is usually determined by declaring that a specific taxon in a phylogeny is an outgroup or by making a molecular clock as- sumption (Felsenstein 2004). The first method assumes that the location of the root is already known, that it is on the edge connected to the outgroup. The second method comes in varying degrees of complexity. In its most simple form it assumes that the tree is ultrametric , that the genetic distance from the root node to each tip is identical. In more sophisticated Bayesian approaches the location of the root enters the calculation as part of the prior distribution of tree topologies and branch lengths, so that the tree is not necessarily ultrametric but that in some sense the evolutionary time from the root of the tree to the tips is the same along every lineage (Drummond and Rambaut 2007). 

A third method is to use a substitution model that is able to identify the location of the root. Such a model must not be reversible, but using a model that is not reversible does not automatically ensure that the model is able to identify the root. This statement is easily justified using the findings in Chang (1996), where a model that is non-stationary, so also non-reversible, is not able to recover the root. That a non-reversible model might not be even theoretically able to discover the root seems to have been missed by some authors. 

Yang and Roberts (1995) fitted a non-stationary model to rooted topologies of real data using maximum likelihood and found that the location of the root of the tree had a significant effect on likelihood estimates. This is useful empirical evidence but the authors made no attempt to prove that their model is identifiable. 

Huelsenbeck et al. (2002) fitted a non-reversible but stationary model to real and simulated data and found that while the outgroup and molecular clock methods were able to recover the location of the root in many cases, their model was not. Again, they made no effort to show that their model is theoretically capable of recovering the location of the root, so the poor performance of their model is not necessarily a reflection on the ability of all substitution models to recover the location of the 

