April10_Support

April10_Support - Integrative Biology 200A"PRINCIPLES OF...

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Integrative Biology 200A “PRINCIPLES OF PHYLOGENETICS” Spring 2008 University of California, Berkeley Kipling Will- 10 Apr Phylogenetic tree IV- Data/Hypothesis Exploration and Support Measures I. Overview. -- The truest tests involve empirical tests that examine all critical evidence. For phylogenetic hypotheses (tree, branching pattern, branch lengths, character state distribution), this involves the addition of more characters and taxa. This is not always reasonable/feasible and when do we have enough anyway? This is an issue of philosophical or statistical confidence. -- The simplest form of confidence, which is somewhat subjective, is to show character state changes on the cladogram. Groups supported by more, less homoplastic and more complex character state changes are thought to be well supported. When more of our initial statements of homology survive and are compatible we have increased confidence in the hypothesis. Alas, this may be suitable for morphological data, however, it is very difficult to apply given the simplicity of DNA sequence. -- A general or specific “fit” to external data (e.g. biogeographic patterns) also builds confidence. However, it is generally more narrative and subjective, making it hard to evaluate if you are not actively working within the system. -- It is necessary to express some sort of confidence or make a statement of reliability in order to give others a sense of how well your data fit your hypothesis and to what degree the critical evidence refutes competing hypotheses even if we are confident in the result. -- Many exploration methods seek some sort of statistical reliability or measure to give a notion of how bold or conservative we should be in regard to conclusions based on the phylogenetic pattern. The fact that a nearby sub- optimal solutions exist is not enough to cause us to move from one hypothesis to another. -- Although there is a general notion that we are identifying well supported clades, exploration methods and support measures are really just as (more?) important for pointing to poorly supported parts of the tree. Poorly supported groups suggest where future efforts need to be applied. -- Most statistical methods require some assumption of a universe from which the sample is drawn. Generally this is random sample of the universe of possible independent entities, i.e. they are independent and identically distributed (i.i.d). II. Sensitivity and Resampling Analyses: Various heuristic methods explore how robust the hypothesis is likely to be if the underlying assumptions are wrong or expressed as some sort of “support”. A. Assumption sensitivity analyses: HOW: Assumptions (= parameters) are varied in multiple analyses and the results compared in some way. WHY: To look for (in)sensitivity to variation in model assumption (e.g. weights assigned to transitions/transversions changes topology). This has been used as an optimality criterion for deciding if a group should be accepted or rejected. Groups sensitive to variation are rejected. Also used as a means to select a set of alignment parameters.
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