Approaches for Assessing Phylogenetic Accuracy

Accuracy of phylogenetic methods may be assessed in terms of consistency, efficiency, and robustness. Four principal methods have been used for assessing phylogenetic accuracy: simulation, known phylogenies, statistical analyses, and congruence studies. Simulation studies are useful for studying accuracy of methods under idealized conditions and can be used to make general predic? tions about the behavior of methods if the limitations of the models are taken into account. Studies of known phylogenies can be used to test predictions from simulation studies, thus providing a check on the robustness of the models (and possibly suggesting refinements for future simulations). Statistical analyses allow general predictions to be applied to specific results, facilitate assessments as to whether or not sufficient data have been collected to formulate a robust conclusion, and indicate whether a given data set is any more structured than random noise. Finally, congruence studies of multiple data sets can be used to assess the degree to which independent results agree and thus the minimum proportion of the findings that can be attributed to an underlying phylogeny. These differ? ent methods of assessing phylogenetic accuracy are largely complementary, and the results are consis? tent in identifying a large class of problems that are amenable to phylogenetic reconstruction. [Phy? logeny; accuracy; simulations; experimental evolution; statistics; congruence; consistency; efficiency; robustness. ] Phylogenetic analyses have become com? monplace throughout the biological disci? plines during the past few decades. This in? creased emphasis on evolutionary history is a direct result of the realization of the im? portance of understanding phylogenetic background as a prerequisite to interpreting virtually any biological system in a compar? ative context. However, the increased uti? lization of phylogenetic approaches has been driven at least as much by technologi? cal and methodological advances as by con? ceptual advances. In particular, advances in algorithm development, computer technol? ogy, and molecular biology have made phy? logenetic analyses feasible for almost any problem involving biological lineages, from viral epidemics in extant human popula? tions (e.g., Ou et al., 1992) to the origins of the earliest lineages of life (e.g., Olsen, 1987). Phylogenetic applications depend on accurate reconstructions of phylogenetic trees; therefore, it is natural that systematists should wonder about the accuracy of their reconstructed trees. This issue of Sys? tematic Biology contains reviews of the four approaches that systematists have explored to examine phylogenetic accuracy and as1 E-mail: hillis@bull.zo. utexas.edu. sess confidence in their results: evolution? ary simulations (Huelsenbeck, 1995), explo? ration of known (observed) phylogenies (briefly reviewed here), statistical evalua? tions (Li and Zharkikh, 1995), and congru? ence studies (Miyamoto and Fitch, 1995). I evaluating phylogenetic accuracy, there are two common goals: one may ask about general properties of phylogenetic methods or about a specific phylogenetic es? timate. A systematist may address how well a given method works under different cir? cumstances (e.g., different evolutionary conditions, different amounts of informa? tion, different types of trees, etc.). Such studies may ask about phylogenetic perfor? mance of a single method or may compare several methods. This approach primarily involves numerical simulations and investi? gation of known phylogenies. On the other hand, statistical and congruence studies tend to address specific questions of phylo? genetic accuracy, i.e., how much confidence can be placed in a specific phylogenetic re? sult? Of course, the distinction is not always clear because simulations can be used to ad? dress confidence in a particular empirical result (e.g., Hillis et al., 1994a) and general conclusions about the relative accuracy of phylogenetic methods can come from statis? tical or congruence studies (e.g., Penny et