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Comparative analysis of otolith morphology in icefishes (Channichthyidae) applying different statistical classification methods

机译:不同统计分类方法的冰氨酸(Channichthyidae)otolith形态的比较分析

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The genus Chionodraco includes three morphologically similar species, two of them (C. hamatus and C. myersi) living sympatrically in the Eastern Antarctica, and the third one (C. rastrospinosus) being distributed in the Western Antarctica along the southern Scotia Arc. The few overlapping diagnostic characters are often useless for the right taxonomic identification of three species, complicated by the presence of hybrids between them. In the present study, we tested the discrimination power of sagittal otolith shape as a tool to distinguish among these closely related species. An array of shape indices and elliptic Fourier descriptors were individually obtained from sagittal otoliths and analysed using three different models for species classification: Linear Discriminant Analysis (LDA), Random Forest (RF) and K-nearest neighbour (KNN). According to the characteristics of the adopted metric (precision), none of the methods resulted to be the absolute best approach as the performance largely varied with the species. The overall precision was 63 %, 66 % and 70 % for LDA, RF and KNN respectively, so that KNN can be considered the best classifier model. As expected, C. myersi was the best recognized species by all classifier models, consistently with its morphological and phylogenetic difference from the other two species. Taking into account the observed pattern of otolith morphology of the three species and their own geographical distribution, we were able to evaluate the role of genetic, endogenous or environmental components in influencing otolith size and shape.
机译:Chionodraco属包括三种形态上类似的物种,其中两个(C. hamatus和C. myersi)在东部的南极洲角肿,第三个(C.RastospInosus)沿着南·斯科舍省南部的南极洲分发。少数重叠的诊断人物对于右分类分类鉴定的三种物种鉴定通常是无用的,在它们之间存在杂种的存在复杂。在本研究中,我们测试了矢状右侧形状的辨别力作为区分这些密切相关的物种的工具。形状指数和椭圆傅里叶描述符阵列从矢状右侧单独获得,并使用三种不同模型进行分析,用于种类分类:线性判别分析(LDA),随机森林(RF)和K最近邻(KNN)。根据所采用的公制(精确)的特点,没有任何方法导致绝对最佳方法,因为性能很大于物种。对于LDA,RF和KNN,整体精度分别为63%,66%和70%,因此KNN可以被认为是最佳的分级器模型。正如预期的那样,C. Myersi是所有分类器模型的最佳认可的物种,始终如一与其他两种物种的形态学和系统发育差异。考虑到三种物种的Otolith形态的观察模式及其自己的地理分布,我们能够评估遗传,内源性或环境部件在影响欧替尔尺寸和形状方面的作用。

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