南美鬼刀鱼(裸子目:无尾鱼科)电子通信信号的进化:基于序列系统发育的系统发育比较研究

Adam R. Smith , Melissa R. Proffitt , Winnie W. Ho , Claire B. Mullaney , Javier A. Maldonado-Ocampo , Nathan R. Lovejoy , José A. Alves-Gomes , G. Troy Smith
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引用次数: 15

摘要

弱电鬼刀鱼(裸子目:无刀鱼科)的电通信信号为理解其行为的进化和生理学提供了一个有价值的模型系统。羽状体产生连续波型电器官放电(eod),用于电定位和通信。排爆的频率和波形,以及瞬态排爆调制(啁啾)的结构,在不同物种之间有很大差异。然而,了解这些信号是如何进化的,由于缺乏一个很好的支持该家族的系统发育,一直受到阻碍。利用3个基因(细胞色素c氧化酶亚基1、重组激活基因2和细胞色素氧化酶B)序列数据,构建了翼龙科13属32种翼龙的分子系统发育。这一系统发育和广泛的翼类信号数据库使我们能够通过祖先状态重建(ASR)和系统发育广义最小二乘(PGLS)模型来研究信号的进化。我们的分子系统发育与最近的另一个基于序列的系统发育基本一致,并确定了五个强大的翼类分支:(i) Sternarchorhamphus + Orthosternarchus, (ii) Adontosternarchus, (iii) Apteronotus + Parapteronotus, (iv) Sternarchorhynchus,以及(v)包括Porotergus, ' Apteronotus ', Compsaraia, Sternarchogiton, Sternarchella和Magosternarchus的大型分支。我们分析了两个翼类物种(Orthosternarchus tamandua和Sternarchorhynchus mormyrus)的新啁啾记录,并将这些物种的数据与我们在系统发育分析中记录的物种数据相结合。一些信号参数是半胚性的(例如低频的排爆和啁啾,但频率调制很少,但会中断排爆),这表明超高频排爆和“大”啁啾是在无蝶类与其他裸子类分化后进化而来的。与之前的研究相比,我们使用新的系统发育进行的PGLS分析表明,某些EOD和chirp参数之间的关系存在系统发育信号。ASR表明,大多数EOD和chirp参数在进化上是不稳定的,即使在近亲物种之间也经常出现多样性。
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Evolution of electric communication signals in the South American ghost knifefishes (Gymnotiformes: Apteronotidae): A phylogenetic comparative study using a sequence-based phylogeny

The electric communication signals of weakly electric ghost knifefishes (Gymnotiformes: Apteronotidae) provide a valuable model system for understanding the evolution and physiology of behavior. Apteronotids produce continuous wave-type electric organ discharges (EODs) that are used for electrolocation and communication. The frequency and waveform of EODs, as well as the structure of transient EOD modulations (chirps), vary substantially across species. Understanding how these signals have evolved, however, has been hampered by the lack of a well-supported phylogeny for this family. We constructed a molecular phylogeny for the Apteronotidae by using sequence data from three genes (cytochrome c oxidase subunit 1, recombination activating gene 2, and cytochrome oxidase B) in 32 species representing 13 apteronotid genera. This phylogeny and an extensive database of apteronotid signals allowed us to examine signal evolution by using ancestral state reconstruction (ASR) and phylogenetic generalized least squares (PGLS) models. Our molecular phylogeny largely agrees with another recent sequence-based phylogeny and identified five robust apteronotid clades: (i) Sternarchorhamphus + Orthosternarchus, (ii) Adontosternarchus, (iii) Apteronotus + Parapteronotus, (iv) Sternarchorhynchus, and (v) a large clade including Porotergus, ‘Apteronotus’, Compsaraia, Sternarchogiton, Sternarchella, and Magosternarchus. We analyzed novel chirp recordings from two apteronotid species (Orthosternarchus tamandua and Sternarchorhynchus mormyrus), and combined data from these species with that from previously recorded species in our phylogenetic analyses. Some signal parameters in O. tamandua were plesiomorphic (e.g., low frequency EODs and chirps with little frequency modulation that nevertheless interrupt the EOD), suggesting that ultra-high frequency EODs and “big” chirps evolved after apteronotids diverged from other gymnotiforms. In contrast to previous studies, our PGLS analyses using the new phylogeny indicated the presence of phylogenetic signals in the relationships between some EOD and chirp parameters. The ASR demonstrated that most EOD and chirp parameters are evolutionarily labile and have often diversified even among closely related species.

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来源期刊
Journal of Physiology-Paris
Journal of Physiology-Paris 医学-神经科学
CiteScore
2.02
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0.00%
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0
审稿时长
>12 weeks
期刊介绍: Each issue of the Journal of Physiology (Paris) is specially commissioned, and provides an overview of one important area of neuroscience, delivering review and research papers from leading researchers in that field. The content will interest both those specializing in the experimental study of the brain and those working in interdisciplinary fields linking theory and biological data, including cellular neuroscience, mathematical analysis of brain function, computational neuroscience, biophysics of brain imaging and cognitive psychology.
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Editorial Automated detection of high-frequency oscillations in electrophysiological signals: Methodological advances Digital hardware implementation of a stochastic two-dimensional neuron model Recent progress in multi-electrode spike sorting methods Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain–computer interfaces
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