R. W. Burroughs, J. F. Parham, B. L. Stuart, P. D. Smits, K. D. Angielczyk
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引用次数: 0
摘要
随着隐性物种的发现越来越频繁,人们开始关注几何形态计量数据是否能够检测到可用于对此类物种进行形态诊断的精细变异模式。我们结合使用了几何形态计量数据和五种有监督的机器学习方法来研究胸甲的形状是否能区分两种可能的隐龟物种:Actinemys marmorata 和 Actinemys pallida。由于其生物地理分布和保护状况,Actinemys 一直是大量研究的焦点。尽管开展了这些工作,但对这两个物种仍然缺乏可靠的形态学诊断。我们在两个数据集上验证了我们的方法,一个数据集包括八个形态各异的蝾螈物种,另一个数据集包括两个蝾螈亚种(T. scripta scripta、T. scripta elegans)。验证测试的分类率接近完美,这表明通过机器学习方法,胸甲形状是区分蝾螈分类群的有效手段。相比之下,同样的方法在对 Actinemys 的一组备选系统地理学和形态学分选方案进行分类时并没有得到很高的分类率。与验证数据集相比,所有的分类假说都表现不佳,没有一个假说能明确地支持 Actinemys 的分类。有两个假说的机器学习表现略好于其余假说。在这两种情况下,这些假说都倾向于将 A. marmorata 和 A. pallida 标本分成两个物种,从而为两个 Actinemys 物种的假说提供了初步的形态学支持。然而,机器学习的结果也强调了Actinemys作为一个整体,其犁板变异水平低于Emydidae中的其他龟类,但这种形态上的保守性的原因尚不清楚。
Morphological Species Delimitation in the Western Pond Turtle (Actinemys): Can Machine Learning Methods Aid in Cryptic Species Identification?
As the discovery of cryptic species has increased in frequency, there has been interest in whether geometric morphometric data can detect fine-scale patterns of variation that can be used to morphologically diagnose such species. We used a combination of geometric morphometric data and an ensemble of five supervised machine learning methods to investigate whether plastron shape can differentiate two putative cryptic turtle species, Actinemys marmorata and Actinemys pallida. Actinemys has been the focus of considerable research due to its biogeographic distribution and conservation status. Despite this work, reliable morphological diagnoses for its two species are still lacking. We validated our approach on two datasets, one consisting of eight morphologically disparate emydid species, the other consisting of two subspecies of Trachemys (T. scripta scripta, T. scripta elegans). The validation tests returned near-perfect classification rates, demonstrating that plastron shape is an effective means for distinguishing taxonomic groups of emydids via machine learning methods. By contrast the same methods did not return high classification rates for a set of alternative phylogeographic and morphological binning schemes in Actinemys. All classification hypotheses performed poorly relative to the validation datasets and no single hypothesis was unequivocally supported for Actinemys. Two hypotheses had machine learning performance that was marginally better than our remaining hypotheses. In both cases, those hypotheses favored a two-species split between A. marmorata and A. pallida specimens, lending tentative morphological support to the hypothesis of two Actinemys species. However, the machine learning results also underscore that Actinemys as a whole has lower levels of plastral variation than other turtles within Emydidae, but the reason for this morphological conservatism is unclear.