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Combined data, Bayesian phylogenetics, and the origin of the New Zealand cicada genera [Review]

机译:组合数据,贝叶斯系统发生学和新西兰蝉属的起源[综述]

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We have applied Bayesian and maximum likelihood methods of phylogenetic estimation to data from four mitochondrial genes (COI, COII, 12S, and 16S) and a single nuclear gene (EF1alpha) from several genera of New Zealand, Australian, and New Caledonian cicada taxa. We specifically focused on the heterogeneity of phylogenetic signal among the different data partitions and the biogeographic origins of the New Zealand cicada fauna. The Bayesian analyses circumvent many of the problems associated with other statistical tests for comparing data partitions. We took an information-theoretic approach to model selection based on the Akaike Information Criterion (AIC). This approach indicated that there was considerable uncertainty in identifying the best-fit model for some of the partitions. Additionally, a large amount of uncertainty was associated with many parameter estimates from the substitution model. However, a sensitivity analysis on the combined dataset indicated that the model selection uncertainty had little effect on estimates of topology because these estimates were largely insensitive to changes in the assumed model. This outcome suggests strong signal in our data. Our analyses support a New Caledonian affiliation of the New Zealand cicada genera Maoricicada, Kikihia, and Rhodopsalta and Australian affinities for the genera Amphipsalta and Notopsalta. This result was surprising, given that previous cicada biologists suspected a close relationship between Amphipsalta, Notopsalta, and Rhodopsalta based on genitalic characters. Relationships among the closely related genera Maoricicada, Kikihia, and Rhodopsalta were poorly resolved, the mitochondrial data and the EF1alpha data favoring different arrangements within this clade.
机译:我们已将贝叶斯和最大似然系统发育估计方法应用于来自四个线粒体基因(COI,COII,12S和16S)和来自新西兰,澳大利亚和新喀里多尼亚蝉类群的几个属的单个核基因(EF1alpha)的数据。我们专门研究了系统差异信号在不同数据分区之间的异质性以及新西兰蝉动物群的生物地理起源。贝叶斯分析规避了与其他统计测试(用于比较数据分区)相关的许多问题。我们基于Akaike信息准则(AIC),采用信息理论方法进行模型选择。这种方法表明,在确定某些分区的最佳拟合模型时存在很大的不确定性。另外,大量的不确定性与来自替代模型的许多参数估计有关。但是,对组合数据集的敏感性分析表明,模型选择的不确定性对拓扑估计几乎没有影响,因为这些估计对假定模型的变化不敏感。这一结果表明我们数据中的强烈信号。我们的分析支持新西兰蝉属毛里卡达,奇基希亚和罗多萨尔塔属的新喀里多尼亚隶属关系,以及澳大利亚对两栖动物和Notopsalta属的亲属关系。考虑到以前的蝉生物学家根据生殖器特征怀疑两栖类,Notopsalta和Rhodopsalta之间的密切关系,这一结果令人惊讶。紧密相关的属毛里卡达属,奇基希亚属和红球菌属之间的关系很难解决,线粒体数据和EF1alpha数据有利于该进化枝内的不同排列。

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