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.