Today we’re going to use PAUP* to generate trees using distance methods

Today we’re going to use PAUP* to generate trees using distance methods. We’ve discussed distance methods in class, and you have learned that they are not the most theoretically justified of methods for inferring phylogenies, although clustering methods do have some uses in other areas of statistics. For several reasons, it is important that you learn how to utilize them. First, you should use them as one of the analyses in your final project, as a comparison to other methods. They will almost always give a different tree than the other optimality criteria, since they are searching for total similarity, and not distinguishing between synapomorphy, symplesiomorphy, and homoplasy. Second, some workers people do feel that they are a good way to infer phylogenies. Finally, they are by far the fastest way to find a tree. Whereas parsimony and likelihood methods have to search through tree space and compare the optimization of the character matrix on many trees, most distance methods use an algorithm to directly generate a tree from the distance matrix. This speed makes it very useful for genomics, where it is often necessary to generate tens of thousands of trees, but getting the exact tree each time is not as important as getting the right tree the vast majority of the time. Distance analysis have two main components: the formulas used to calculate the distances (a.k.a. distance measures) and the algorithms used to compute a tree from the distances.