Department of Ecology and Evolutionary Biology, University of California, Los Angeles, USA.
Research and Collections, Section of Ichthyology, Natural History Museum of Los Angeles County, CA, USA; Vertebrate Zoology, Santa Barbara Museum of Natural History, CA, USA.
Mol Phylogenet Evol. 2020 Oct;151:106862. doi: 10.1016/j.ympev.2020.106862. Epub 2020 May 28.
Gobies, sleepers, and cardinalfishes represent major clades of a species rich radiation of small bodied, ecologically diverse percomorphs (Gobiaria). Molecular phylogenetics has been crucial to resolving broad relationships of sleepers and gobies (Gobioidei), but the phylogenetic placements of cardinalfishes and nurseryfishes, as reciprocal or sequential sister clades to Gobioidei, are uncertain. In order to evaluate relationships among and within families we used a phylogenetic data mining approach to generate densely sampled trees inclusive of all higher taxa. We utilized conspecific amino acid homology to improve alignment accuracy, included ambiguously identified taxa to increase taxon sampling density, and resampled individual gene alignments to filter rogue sequences before concatenation. This approach yielded the most comprehensive tree yet of Gobiaria, inferred from a sparse (17 percent-complete) supermatrix of one ribosomal and 22 protein coding loci (18,065 characters), comprised of 50 outgroup and 777 ingroup taxa, representing 32 percent of species and 68 percent of genera. Our analyses confirmed the lineage-based classification of gobies with strong support, identified sleeper clades with unforeseen levels of systematic uncertainty, and quantified competing phylogenetic signals that confound resolution of the root topology. We also discovered that multilocus data completeness was related to maximum likelihood branch support, and verified that the phylogenetic uncertainty of shallow relationships observed within goby lineages could largely be explained by supermatrix sparseness. These results demonstrate the potential and limits of publicly available sequence data for producing densely-sampled phylogenetic trees of exceptionally biodiverse groups.
虾虎鱼、睡鲨和隆头鱼代表了小型身体、生态多样化的鲈形目鱼类（虾虎鱼科）丰富辐射的主要分支。分子系统发生学对于解决睡鲨和虾虎鱼（虾虎鱼目）的广泛关系至关重要，但隆头鱼和隆头鱼科，作为虾虎鱼目的相互或连续姐妹分支的系统发育位置不确定。为了评估家族之间和内部的关系，我们使用了一种系统发育数据挖掘方法来生成包括所有高等分类群的密集采样树。我们利用同源氨基酸来提高对齐精度，包括模糊识别的分类单元以增加分类单元采样密度，并在串联之前重新采样单个基因对齐以过滤流氓序列。这种方法产生了迄今为止最全面的虾虎鱼科树，它是从一个稀疏（17%完整）的核糖体和 22 个蛋白质编码基因座的超矩阵（18065 个字符）推断而来的，其中包括 50 个外群和 777 个内群分类单元，代表了 32%的物种和 68%的属。我们的分析证实了基于谱系的虾虎鱼分类具有很强的支持，确定了睡鲨类群具有意想不到的系统不确定性，并量化了混淆根拓扑分辨率的竞争系统发育信号。我们还发现，多基因座数据的完整性与最大似然分支支持有关，并验证了在虾虎鱼谱系内观察到的浅层关系的系统发育不确定性在很大程度上可以用超矩阵稀疏度来解释。这些结果表明，公共可用序列数据在生成具有极高生物多样性的群体的密集采样系统发育树方面具有潜力和局限性。
