1 "PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2011 University of California, Berkeley B.D. Mishler Jan. 20, 2011. Phylogenetic reconstruction in a nutshell: trees I. Summary of previous lecture: Hennigian phylogenetics can be most tersely described as the study of homology and its implications (Patterson, 1982). The basic criteria of character analysis, discussed last time, amount to a joint assumption that an apparent taxic homology [N.B., this a feature that has already passed strict observational and experimental tests of detailed similarity, heritability, discrete states, and independence] is more likely to be due to true taxic homology than to homoplasy, unless evidence to the contrary exists, i.e., a majority of apparent taxic homologies showing a different pattern. We assemble a matrix of hypothesized homologies, and evaluate them relative to each other. This requires that we can build well-supported phylogenetic trees from the matrix, the subject for today. II. Trees -- what are they, really, and what can go wrong? Here are some important initial questions for discussion: What are phylogenetic trees, really? What do you see when you look closely at a branch? -- the fractal nature of phylogeny (is there a smallest level?) What is the relationship between characters and trees? Characters and OTUs? Characters and levels? The tree of life is inherently fractal, which complicates the search for answers to these questions. Look closely at one lineage of a phylogeny and it dissolves into many separate lineages, and so on down to a very fine scale. Thus the nature of both OTU's ("operational taxonomic units," the "twigs" of the tree in any particular analysis) and characters (hypotheses of homology, markers that serve as evidence for the past existence of a lineage) change as one goes up and down this fractal scale. Furthermore, there is a tight interrelationship between OTUs and character states, since they are reciprocally recognized during the character analysis process. III. Tree-building; Algorithms & Assumptions; reconstruction vs. estimation ?? What is the best way to turn a matrix into a tree? This question has many different answers for different investigators, depending on their background. A. Phenetic (distance) • These methods work from an intermediate distance or similarity matrix2 • Disadvantages: -- usually assumes molecular clock. -- many distance measures used are non-metric, therefore one can't interpret branch lengths in terms of actual evolutionary events (Euclidean distance vs. Manhattan Distance). -- hides homoplasy. -- throws away the information on individual characters that was so laboriously obtained. • Advantages: -- ??? (at best able to mimic the results of a phylogenetic analysis) -- Avoid circularity by not considering evolution? -- Averaging across whole genome? -- Avoiding problem of reticulation? (some argue phenetic methods are OK below species level, as in the field of "phylogeography" -- more later in the class). • Bottom line: distance methods not recommended for the purpose of hypothesizing phylogenies, although of course they are very useful for other tasks in ecology and evolution. B. Cladistic or Phylogenetic (using the information from individual characters directly) These methods build phylogenetic hypotheses directly from the data matrix described last time. There are two main schools of thought, that have converged from different historical beginnings: • The Hennigian phylogenetic systematics tradition, derived from comparative anatomy and morphology, focuses on the implications of individual homologies. This tradition tends to conceive of the inference process as one of reconstructing history following deductive-analytic procedures. The goal is seen as coming up with the best supported hypothesis to explain a unique past event. The "reconstruction" school of thought. • The population genetic tradition, derived from studies of the fate of genes in populations, tends to see phylogenetic inference as a statistical estimation problem. The goal is seen to be choosing a set of trees out of a statistical universe of possible trees, while putting confidence limits on the choice. The "estimation" school of thought. **There is a need to be clear about what statistical approaches are appropriate for a particular situation, or even whether any such approach is appropriate.** • Controversy remains on the applicability of various statistical approaches (or even the desirability of such approaches). Issues under debate include: 1. The nature of the statistical universe being sampled and exactly what evolutionary assumptions are safe to use in hypothesis testing. Under standard views of hypothesis testing, one is interested in evaluating an estimate of some real but unknown parameter, based on samples taken from a relevant class of individual objects (the statistical universe).3 2. It might be argued that a particular phylogeny is one of many possible topologies, thus somehow one might talk about the probability of existence of that topology or of some particular branches. However, phylogenies are unique historical events ("individuals" in the sense of Hull, 1980) ; a particular phylogeny clearly is a member of a statistical universe of one. It is of course valid to try to set a frequency-based probability for such phylogenetic questions as: How often should we expect to find completely pectinate cladograms? or How often should we find a clade as well supported as the mammals? In such cases, there is a valid reference class ("natural kind" in the sense of Hull, 1980) about which one can attempt an inference. 3. It could be reasonably argued that characters in a particular group of organisms are sampled from a universe of possible characters. The counter-argument, however, is that characters are chosen based on a refined set of criteria of likely informativeness, e.g., presence of discrete states, invariance within OTUs, ability to determine potential homology (including alignability for molecular data). Therefore, the characters are at best a highly non-random sample of the possible descriptors of the organisms. It may perhaps be better
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