The main features of the phylogeny program TNT are discussed. Windows versions have a menu interface, while Macintosh and Linux versions are command‐driven. The program can analyze data sets with discrete (additive, non‐additive, step‐matrix) as well as continuous characters (evaluated with Farris optimization). Effective analysis of large data sets can be carried out in reasonable times, and a number of methods to help identifying wildcard taxa in the case of ambiguous data sets are implemented. A variety of methods for diagnosing trees and exploring character evolution is available in TNT, and publication‐quality tree‐diagrams can be saved as metafiles. Through the use of a number of native commands and a simple but powerful scripting language, TNT allows the user an enormous flexibility in phylogenetic analyses or simulations.© The Willi Hennig Society 2008.

The problem of character weighting in cladistic analysis is revisited. The finding that, in large molecular data sets, removal of third positions (with more homoplasy) decreases the number of well supported groups has been interpreted by some authors as indicating that weighting methods are unjustified. Two arguments against that interpretation are advanced. Characters that collectively determine few well‐supported groups may be highly reliable when taken individually (as shown by specific examples), so that inferring greater reliability for sets of characters that lead to an increase in jackknife frequencies may not always be warranted. But even if changes in jackknife frequencies can be used to infer reliability, we demonstrate that jackknife frequencies in large molecular data sets are actually improved when downweighting characters according to their homoplasy but using properly rescaled functions (instead of the very strong standard functions, or the extreme of inclusion/exclusion); this further weakens the argument that downweighting homoplastic characters is undesirable. Last, we show that downweighting characters according to their homoplasy (using standard homoplasy‐weighting methods) on 70 morphological data sets (with 50–170 taxa), produces clear increases in jackknife frequencies. The results obtained under homoplasy weighting also appear more stable than results under equal weights: adding either taxa or characters, when weighting against homoplasy, produced results more similar to original analyses (i.e., with larger numbers of groups that continue being supported after addition of taxa or characters), with similar or lower error rates (i.e., proportion of groups recovered that subsequently turn out to be incorrect). Therefore, the same argument that had been advanced against homoplasy weighting in the case of large molecular data sets is an argument in favor of such weighting in the case of morphological data sets.© The Willi Hennig Society 2008.

Quantitative and continuous characters have rarely been included in cladistic analyses of morphological data; when included, they have always been discretized, using a variety of ad hoc methods. As continuous characters are typically additive, they can be optimized with well known algorithms, so that with a proper implementation they could be easily analyzed without discretization. The program TNT has recently incorporated algorithms for analysis of continuous characters. One of the problems that has been pointed out with existing methods for discretization is that they can attribute different states to terminals that do not differ significantly—or vice versa. With the implementation in TNT, this problem is diminished (or avoided entirely) by simply assigning to each terminal a range that goes from the mean minus one (or two) SE to the mean plus one (or two) SE; given normal distributions, terminals that do not overlap thus differ significantly (more significantly if using more than 1 SE). Three real data sets (for scorpions, spiders and lizards) comprising both discrete and quantitative characters are analyzed to study the performance of continuous characters. One of the matrices has a reduced number of continuous characters, and thus continuous characters analyzed by themselves produce only poorly resolved trees; the support for many of the groups supported by the discrete characters alone, however, is increased when the continuous characters are added to the analysis. The other two matrices have larger numbers of continuous characters, so that the results of separate analyses for the discrete and the continuous characters can be more meaningfully compared. In both cases, the continuous characters (analyzed alone) result in trees that are relatively similar to the trees produced by the discrete characters alone. These results suggest that continuous characters carry indeed phylogenetic information, and that (if they have been observed) there is no real reason to exclude them from the analysis.© The Willi Hennig Society 2006.