Noise

Abstract

The proliferation of DNA sequence data has generated a concern about the effects of “noise” on phylogeny reconstruction. This concern has led to various recommendations for weighting schemes and for separating data types prior to analysis. A new technique is explored to examine directly how noise influences the stability of parsimony reconstruction. By appending purely random characters onto a matrix of pure signal, or by replacing characters in a matrix of signal by random states, one can measure the degree to which a matrix is robust against noise. Reconstructions were sensitive to tree topology and clade size when noise was added, but were less so when character states were replaced with noise. When a signal matrix is complemented with a noise matrix of equal size, parsimony will trace the original signal about half the time when there is only one synapomorphy per node, and about 90% of the time when there are three synapomorphies per node. Similar results obtain when 20% of a matrix is replaced by noise. Successive weighting does not improve performance. Adding noise to only some taxa is more damaging, but replacing characters in only some taxa is less so. The bootstrap and g1 (tree skewness) statistics are shown to be uninterpretable measures of noise or departures from randomness. Empirical data sets illustrate that commonly recommended schemes of differential weighting (e.g., downweighting third positions) are not well supported from the point of view of reducing the influence of noise nor are more noisy data sets likely to degrade signal found in less noisy data sets.