Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.
Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Nova Scotia B3H 4H7, Canada.
Syst Biol. 2021 Jun 16;70(4):838-843. doi: 10.1093/sysbio/syab001.
Long branch attraction (LBA) is a prevalent form of bias in phylogenetic estimation but the reasons for it are only partially understood. We argue here that it is largely due to differences in the sizes of the model spaces corresponding to different trees. Trees with long branches together allow much more flexible internal branch length parameter estimation. Consequently, although each tree has the same number of parameters, trees with long branches together have larger effective model spaces. The problem of LBA becomes particularly pronounced with partitioned data. Formulation of tree estimation as model selection leads us to propose bootstrap bias corrections as cross-checks on estimation when long branches end up being estimated together. [Bootstrap; long branch attraction; maximum likelihood; model selection; partitioned model; phylogenetics.].
长枝吸引（LBA）是系统发育估计中普遍存在的偏差形式，但造成这种偏差的原因尚不完全清楚。我们认为，这主要是由于不同树对应的模型空间大小不同所致。具有长枝的树可以允许更灵活的内部分支长度参数估计。因此，尽管每棵树的参数数量相同，但具有长枝的树的有效模型空间更大。当长枝最终一起被估计时，分区数据会使 LBA 问题变得尤为明显。将树估计表述为模型选择，这使我们提出了自举偏差校正，作为在长枝一起被估计时对估计的交叉检查。[自举；长枝吸引；最大似然；模型选择；分区模型；系统发生学。]
