PARSIMONY JACKKNIFING OUTPERFORMS NEIGHBOR‐JOINING

Abstract

Abstract- Because they are designed to produced just one tree, neighbor-joining programs can obscure ambiguities in data. Ambiguities can be uncovered by resampling, but existing neighbor-joining programs may give misleading bootstrap frequencies because they do not suppress zero-length branches and/or are sensitive to the order of terminals in the data. A new procedure, parsimony jackknifing, overcomes these problems while running hundreds of times faster than existing programs for neighbor-joining bootstrapping. For analysis of large matrices, parsimony jackknifing is hundreds of thousands of times faster than extensive branch-swapping, yet is better able to screen out poorly-supported groups.