Rohlf F James, Chang W S, Sokal R R, Kim Junhyong
Department of Ecology and Evolution, State University of New York, Stony Brook, NY, 11794, USA.
Evolution. 1990 Sep;44(6):1671-1684. doi: 10.1111/j.1558-5646.1990.tb03855.x.
A simulation study was carried out to investigate the relative importance of tree topology (both balance and stemminess), evolutionary rates (constant, varying among characters, and varying among lineages), and evolutionary models in determining the accuracy with which phylogenetic trees can be estimated. The three evolutionary context models were phyletic (characters can change at each simulated time step), speciational (changes are possible only at the time of speciation into two daughter lineages), and punctuational (changes occur at the time of speciation but only in one of the daughter lineages). UPGMA clustering and maximum parsimony ("Wagner trees") methods for estimating phylogenies were compared. All trees were based on eight recent OTUs. The three evolutionary context models were found to have the largest influence on accuracy of estimates by both methods. The next most important effect was that of the stemminess × context interaction. Maximum parsimony and UPGMA performed worst under the punctuational models. Under the phyletic model, trees with high stemminess values could be estimated more accurately and balanced trees were slightly easier to estimate than unbalanced ones. Overall, maximum parsimony yielded more accurate trees than UPGMA-but that was expected for these simulations since many more characters than OTUs were used. Our results suggest that the great majority of estimated phylogenetic trees are likely to be quite inaccurate; they underscore the inappropriateness of characterizing current phylogenetic methods as being for reconstruction rather than for estimation.
进行了一项模拟研究，以调查树形拓扑结构（平衡性和分支性）、进化速率（恒定、字符间变化和谱系间变化）以及进化模型在确定系统发育树估计准确性方面的相对重要性。三种进化背景模型分别是系统发育模型（在每个模拟时间步长字符都可能发生变化）、物种形成模型（变化仅在物种形成进入两个子谱系时才可能发生）和间断模型（变化发生在物种形成时，但仅在一个子谱系中）。比较了用于估计系统发育的UPGMA聚类法和最大简约法（“瓦格纳树”）。所有树均基于八个最近的分类单元。结果发现，这三种进化背景模型对两种方法估计准确性的影响最大。其次最重要的影响是分支性×背景相互作用的影响。在间断模型下，最大简约法和UPGMA表现最差。在系统发育模型下，具有高分支性值的树可以更准确地估计，并且平衡树比不平衡树稍微更容易估计。总体而言，最大简约法产生的树比UPGMA更准确——但对于这些模拟来说这是预期的，因为使用的字符比分类单元多得多。我们的结果表明，绝大多数估计的系统发育树可能相当不准确；它们强调了将当前系统发育方法描述为用于重建而非估计的不恰当性。
