However, when the level of sequence divergence is low, this is not the case. Under this principle, the preferred tree is determined by assigning an optimality score to all possible topologies (or all potentially correct topologies) according to a certain procedure and choosing the topology that shows the highest or lowest optimal score.
For example, in the case of maximum-parsimony (MP) methods (Eck and Dayhoff 1966 ), the minimum number of evolutionary changes that explains the entire sequence evolution (tree length [TL]) is computed for each topology, and the topology showing the smallest TL value is chosen as the preferred tree (MP tree).
We show that when ME methods are used, the simple p distance generally gives better results in phylogenetic inference than more complicated distance measures such as the Hasegawa-Kishino-Yano (HKY) distance, even when nucleotide substitution follows the HKY model.
When ML methods are used, the simple Jukes-Cantor (JC) model of phylogenetic inference generally shows a better performance than the HKY model even if the likelihood value for the HKY model is much higher than that for the JC model.
This indicates that at least in the present case, selecting of a substitution model by using the likelihood ratio test or the AIC index is not appropriate.
When and the extent of sequence divergence is high, the NJ method with p distance often shows a better performance than ML methods with the JC model.
In phylogenetic inference by maximum-parsimony (MP), minimum-evolution (ME), and maximum-likelihood (ML) methods, it is customary to conduct extensive heuristic searches of MP, ME, and ML trees, examining a large number of different topologies.
However, these extensive searches tend to give incorrect tree topologies.Here we show by extensive computer simulation that when the number of nucleotide sequences () is relatively small, the simple MP or ML tree search algorithms such as the stepwise addition (SA) plus nearest neighbor interchange (NNI) search and the SA plus subtree pruning regrafting (SPR) search are as efficient as the extensive search algorithms such as the SA plus tree bisection-reconnection (TBR) search in inferring the true tree.In the case of ME methods, the simple neighbor-joining (NJ) algorithm is as efficient as or more efficient than the extensive NJ+TBR search.For this reason, a number of heuristic search algorithms that attempt to find the optimal tree are currently used (Swofford and Begle 1993) is large, the optimality scores of the MP and ME trees are always smaller than or equal to those of the true tree, whereas the optimality score of the ML tree is always greater than or equal to that of the true tree.This indicates that the optimization principle tends to give incorrect topologies when also showed that for obtaining the true tree (not the optimal tree), simple topology search algorithms are often as efficient as extensive search algorithms.In the case of ME methods, a simple algorithm called neighbor joining (NJ; Saitou and Nei 1987 ) was shown to be as efficient as the standard ME method in almost all cases examined.