Noise

The proliferation of DNA sequence data has generated a concern about the ef fects of "noise" on phylogeny reconstruction. This concern has led to vario us recommendations for weighting schemes and for separating data types prio r to analysis. A new technique is explored to examine directly how noise in fluences the stability of parsimony reconstruction. By appending purely ran dom 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 topo logy and clade size when noise was added, but were less so when character s tates 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 o btain when 20% of a matrix is replaced by noise. Successive weighting does not improve performance. Adding noise to only some taxa is more damaging, b ut replacing characters in only some taxa is less so. The bootstrap and gl (tree skewness) statistics are shown to be uninterpretable measures of nois e or departures from randomness. Empirical data sets illustrate that common ly recommended schemes of differential weighting (e.g., downweighting third positions) are not well supported from the point of view of reducing the i nfluence of noise nor are more noisy data sets likely to degrade signal fou nd in less noisy data sets. (C) 1999 The Willi Hennig Society.