- Most phylogenetic methods will
*always*find at least one tree- Consider how the analyses are done:
- Choose an optimality criterion
- Test a number of alternative topologies against that criterion
- Even random data will have some 'best' tree

- Consider how the analyses are done:
- The importance of independent lines of evidence
- Do morphological and molecular data agree?
- Do two genes agree?

- Most methods of phylogenetic analysis share many assumptions
- Therefore, different analytical method is not an independent line of evidence!

- Because it is difficult to get independent lines of evidence, we want to
assess confidence in the tree that we have got.
- Tree length distributions
- Equally parsimonious trees
- Relaxation of optimality criterion
- Bremer Index/Decay Analysis

- Text p. 507 - 509

- Build a new data matrix by randomly sampling characters with replacement
- Take the original data matrix
- Sample the matrix, randomly copying one character (column) from the
original matrix
- Do
*not*delete the character after copying it - Each taxon's character state for the sampled character remains as it was in the original matrix

- Do
- Add the selected character to a new data matrix
- Repeat sampling until the new data matrix has as many characters as
the original
- Some characters will be sampled more than once, others not at all
- The new dataset (a
**pseudosample**) contains the same number of characters as the original data set, and the taxa included are unchanged.

- Perform full phylogenetic analysis
- Repeat many times
- The higher the number of replicates, the more precise the bootstrap values will be
- But remember the difference between
**accuracy**and**precision**

- Calculate frequency with which taxon
**bipartitions**(branches) appear in the new analyses (these frequencies are often reported as percentages)- Any tree can be thought of as a set of bipartitions
- Calculate frequency for each taxon bipartition that is found during replication

- What bootstrap values mean
- Boostrapping measures how
*consistently*the data support given taxon bipartitions - High bootstrap values (close to 100%) mean uniform support
- i.e., if the bootstrap value for a certain clade is close to 100%, nearly all of the characters informative for this group agree that it is a group.

- Boostrapping measures how
- Pitfalls:
- Does not indicate whether or not the tree is 'correct'
- Will be mislead by 'long branch attraction'
- Slow, especially with messy data
- Low bootstrap values (below 50%) are essentially meaningless
- Every psuedosample's analysis must be performed correctly
- In big analyses, may not be practical to find the best tree for each psuedosample

- Randomly delete characters until a given fraction (usually half) have been removed
- Advantages
- No character is represented more than once

- Disadvantages
- Size of data matrix is different

- Anecdotal
- Kishono-Hasegawa test (p. 505)
- g1 statistics
- Likelihood ratio tests
- d=2(lnL1-lnL0)
- Applicable to tree topology only under limited conditions, e.g., when one topology is a subset of the other, or perhaps when they differ only by the placement of a single branch.

- Simulation methods