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Newer methods for tree building
Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria, ignore synapomorphy and symplesiomorphy, and pull root out on tree using outgroup All computationally intense
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Newer methods for tree building Search Criteria
Parsimony Analysis = build unrooted tree with the fewest character state changes Maximum likelihood = given rules about how DNA sequences change over time, a tree can be found that reflects the most likely sequence of events Bayesian inference = generates a posterior distribution for a parameter, composed of a phylogenetic tree and a model of evolution, based on the prior for that parameter and the likelihood of the data
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Pretty hard to do by hand…
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Rooted vs. unrooted trees
A B C D e f A C e f g B D Basal node obvious vs. basal node unknown Rooted trees are way more fun! Just need to know which node is basal (oldest) Imagine pulling strings
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Rooted vs. unrooted trees
Basal node obvious, basal node unknown
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Number of unrooted trees for 4 taxa = 3
C A B B D C D A C D B
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Number of unrooted trees for 4 taxa = 3 Number of rooted trees for 4 taxa = 15
C A B B D Practice rooting C D A C D B
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Unrooted Tree of Herpes Viruses
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Number of trees increases with number of taxa
4 taxa 3 unrooted trees 8 taxa 10,395 unrooted trees 10 taxa 2,027,025 unrooted trees 22 taxa 3 x 1023 (almost a mole) 50 taxa 3 x 1074 (More trees than the number of atoms in the universe) “Exhaustive searches” of tree topologies are nearly impossible with modern data sets Algorithms attempt to find best tree anyway Barry Hall, Phylogenetic Trees Made Easy
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Estimating confidence in trees:
Character conflict is common in data. We want to know if it is overwhelming the signal in the data. We may not have collected enough data to estimate relationships confidently.
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Estimating confidence in trees:
Bootstrap support If low, mentally collapse the node into a polytomy
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Breaking the Bifurcation Rule: Hard polytomies and Soft polytomies
Cichlids
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Venereal syphilis
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Non-venereal Trepanomatoses
1950 million people
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Non-venereal Trepanomatoses
1950
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Endemic Trepanomatoses
1950 50-100 million 1970 1990 = 2.5 million
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