4.7 Article

A mitochondrial genome phylogeny of owlet moths (Lepidoptera: Noctuoidea), and examination of the utility of mitochondrial genomes for lepidopteran phylogenetics

期刊

MOLECULAR PHYLOGENETICS AND EVOLUTION
卷 85, 期 -, 页码 230-237

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ympev.2015.02.005

关键词

Noctuoidea; Mitochondrial genome; Phylogeny; MAFFT; Gblocks; Gaps

资金

  1. National Natural Science Foundation of China [31272288, 31071952, 31372176]
  2. Key Laboratory of the Zoological Systematics and Evolution of the Chinese Academy of Sciences [O529YX5105]
  3. ERC (Belgium) [250325]
  4. Australian Research Council [FT120100746]
  5. European Research Council (ERC) [250325] Funding Source: European Research Council (ERC)

向作者/读者索取更多资源

A phylogenetic hypothesis for the lepidopteran superfamily Noctuoidea was inferred based on the complete mitochondrial (mt) genomes of 12 species (six newly sequenced). The monophyly of each noctuoid family in the latest classification was well supported. Novel and robust relationships were recovered at the family level, in contrast to previous analyses using nuclear genes. Erebidae was recovered as sister to (Nolidae + (Euteliidae + Noctuidae)), while Notodontidae was sister to all these taxa (the putatively basalmost lineage Oenosandridae was not included). In order to improve phylogenetic resolution using mt genomes, various analytical approaches were tested: Bayesian inference (BI) vs. maximum likelihood (ML), excluding vs. including RNA genes (rRNA or tRNA), and Gblocks treatment. The evolutionary signal within mt genomes had low sensitivity to analytical changes. Inference methods had the most significant influence. Inclusion of tRNAs positively increased the congruence of topologies, while inclusion of rRNAs resulted in a range of phylogenetic relationships varying depending on other analytical factors. The two Gblocks parameter settings had opposite effects on nodal support between the two inference methods. The relaxed parameter (GBRA) resulted in higher support values in BI analyses, while the strict parameter (GBDH) resulted in higher support values in ML analyses. (C) 2015 Elsevier Inc. All rights reserved.

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