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 finding trees. However, it is important that you learn how to utilize them. First you should use them as one of the analyses in your paper. Also some people do feel that they are a good way to find trees. 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. There are two different ways that distance analyses may differ. We can use different formulas to calculate the distances, we will cover this first. Once we have a distance matrix we can use different algorithms to generate the tree. I have included a lot of math in this one. You do not have to memorize it. It is only there for the benefit of those of you who are interested. However, you should understand the assumptions that go into each distance measure.