. The Compleat cladist : a primer of phylogenetic procedures. Cladistic analysis; Zoology -- Classification; Phylogeny. 66 KU MUSEUM OF NATURAL HISTORY, SPECIAL PUBLICATION No. 19 EXERCISE —Use the tree in Fig. to find the MPR set for each ancestor. Use these sets to optimize the tree with DEUTRAN. Then optimize using ACCTRAN. 0 0. Fig. —Tree for MPR sets and DELTRAN optimization (Exercise ). Current Technology Modem computer packages like PA UP (Swofford. 1990) are composed of programs containing a wealth of options. Actual analyses may take seconds, hours, or days. Interes
. The Compleat cladist : a primer of phylogenetic procedures. Cladistic analysis; Zoology -- Classification; Phylogeny. 66 KU MUSEUM OF NATURAL HISTORY, SPECIAL PUBLICATION No. 19 EXERCISE —Use the tree in Fig. to find the MPR set for each ancestor. Use these sets to optimize the tree with DEUTRAN. Then optimize using ACCTRAN. 0 0. Fig. —Tree for MPR sets and DELTRAN optimization (Exercise ). Current Technology Modem computer packages like PA UP (Swofford. 1990) are composed of programs containing a wealth of options. Actual analyses may take seconds, hours, or days. Interest- ingly, most of the time spent in analyzing the data is not spent on tree building per se but on evaluating tree topologies using a relevant optimality criterion. The approach to finding the optimal tree can differ considerably from traditional methods. Swofford and Olsen (1990) characterize three general approaches: 1) exhaustive searches, 2) branch-and-bound searches, and 3) heuristic searches. The first two can provide an exact soludon if the data matrix is small enough. By exact solution we mean that the resulting tree or set of trees will be the shortest tree(s) for the data given maximum parsimony as the criterion. The heuristic search will find a short tree(s), but there is no guarantee that the tree will be the shortest. Heuristic searches are performed on much larger data sets instead of using searches guaranteed to find an exact solution because for many large data sets there are no other alternatives (see below). The following abbreviated characterization of these three ap- proaches is abstracted from Swofford and Olsen (1990). 1. Exhaustive search.—An exhaustive search consists of evaluating the data over all possible trees. Because there are no other possible topologies unevaluated, the shortest tree or set of trees will be found. As you will appreciate when you get to Chapter 6, the number of possible tree topologies increases at a very rapid rate. An exha
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