Dillman Casey B, Sidlauskas Brian L, Vari Richard P
Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, PO Box 37012, MRC-159, Washington, DC, 20013-7012, USA.
Department of Fisheries and Wildlife, Oregon State University, 104 Nash Hall, Corvallis, OR, 97331-3803, USA.
Cladistics. 2016 Jun;32(3):276-296. doi: 10.1111/cla.12127. Epub 2015 Jul 14.
Although 11 studies have addressed the systematics of the four families and 281 fish species of the ecomorphologically diverse Anostomoidea, none has proposed a global hypothesis of relationships. We synthesized these studies to yield a supermatrix with 463 morphological characters among 174 ingroup species, and inferred phylogeny with parsimony and Bayesian optimization. We evaluated the applicability of the supermatrix approach to morphological datasets, tested its sensitivity to missing data, determined the impact of homoplastic characters on phylogenetic resolution, and determined the distribution of homologies and homoplasies on the topology. Despite more than 60% missing data, analyses supported the monophyly of all families, and phylogenetic structure degraded only with inclusion of species with high percentages of missing data and in analyses limited to homoplasies. The latter differs modestly from the full matrix indicating phylogenetic signal in homoplastic characters. Character distributions differ across the phylogeny, with a greater prevalence of homologies at deeper nodes and homoplasies nearer the tips than expected by chance. This may suggest early diversification into distinct bauplans with subsequent diversification of faster evolving character systems. The morphological supermatrix approach is powerful and allows integration of classical data with modern methods to examine the evolution of multiple character systems.
尽管已有11项研究探讨了生态形态多样的无齿脂鲤科四个家族及281种鱼类的系统学，但尚无研究提出一个全面的亲缘关系假说。我们综合这些研究，构建了一个包含174个类群物种、463个形态特征的超级矩阵，并通过简约法和贝叶斯优化法推断系统发育关系。我们评估了超级矩阵方法对形态数据集的适用性，测试了其对缺失数据的敏感性，确定了同塑性特征对系统发育分辨率的影响，并确定了同源性和同塑性在拓扑结构上的分布。尽管存在超过60%的缺失数据，但分析结果支持所有家族为单系群，且只有在纳入缺失数据比例高的物种以及仅限于同塑性特征的分析中，系统发育结构才会退化。后者与完整矩阵略有不同，表明同塑性特征中存在系统发育信号。性状分布在整个系统发育过程中有所不同，与随机预期相比，在较深节点处同源性更为普遍，而在靠近末端处同塑性更为普遍。这可能表明早期分化为不同的体型模式，随后快速进化的性状系统发生了多样化。形态超级矩阵方法功能强大，能够将经典数据与现代方法相结合，以研究多个性状系统的进化。
