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Bootstrapping

Bootstrapping. Method for assessing confidence in nodes of a tree Create a new data set by resampling characters randomly with replacement New data set is the same size as the original Variation found from this method is similar to what would be found by collecting new data sets

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Bootstrapping

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  1. Bootstrapping • Method for assessing confidence in nodes of a tree • Create a new data set by resampling characters randomly with replacement • New data set is the same size as the original • Variation found from this method is similar to what would be found by collecting new data sets • Assumes characters evolve independently

  2. Other Methods • Block-bootstrap: resample blocks of characters (used when there is correlated evolution in adjacent characters) • Jackknife: Samples a random half of the data set, characters not duplicated (very similar to bootstrapping) • For all methods, the minimum value typically reported in 70% bootstrap confidence

  3. Consensus Trees • Analyze the multiple data sets created from bootstrapping and determine most likely single tree

  4. Consensus Trees • Majority Rule: Any set of species that appears in more than 50% of the tree is included. The program then considers the other sets in order of frequency, adding to the tree until it is fully resolved. • Strict: A set of species must appear in all input trees to be included.

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