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Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania.
BMC Biology ( IF 5.4 ) Pub Date : 2020-09-03 , DOI: 10.1186/s12915-020-00832-1
Norman MacLeod 1 , Liora Kolska Horwitz 2
Affiliation  

Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures. Our results reveal no evidence for statistically significant sexual size dimorphism, but significant sex-mediated shape dimorphism. These are consistent with the findings of prior wolf sexual dimorphism studies and extend these studies by identifying new aspects of dimorphic variation. Additionally, our results suggest that shape-based sexual dimorphism in the C. lupus cranial complex may be more widespread morphologically than had been appreciated by previous researchers. Our results suggest that size and shape dimorphism can be detected in small samples and may be dissociated in mammalian morphologies. This result is particularly noteworthy in that it implies there may be a need to refine allometric hypothesis tests that seek to account for phenotypic sexual dimorphism. The methods we employed in this investigation are fully generalizable and can be applied to a wide range of biological materials and could facilitate the rapid evaluation of a diverse array of morphological/phenomic hypotheses.

中文翻译:

用于测试小样本形态变异模式的机器学习策略:灰狼(Canis lupus)颅骨的性别二态性。

哺乳动物两性二态性的研究传统上涉及特定骨骼元素的选定尺寸的测量和单一数据分析程序的使用。因此,此类研究受到各种实践和概念的限制。为了比较和对比从更具探索性、多因素的形态变异定量评估方法中可能获得的结果,通过颅骨轮廓的椭圆傅立叶分析对现代以色列灰狼(Canis lupus)颅骨的小样本图像进行了分析,朴素贝叶斯机器学习方法分析这些相同的轮廓数据,并对整个图像进行深度学习分析,其中代表了这些颅骨形态的所有方面。使用 bootstrap 和 jackknife 程序测试每个判别结果的统计显着性和稳定性。我们的结果表明,没有证据表明性别大小二态性具有统计显着性,但性别介导的形状二态性却具有显着性。这些与先前狼性二态性研究的结果一致,并通过识别二态性变异的新方面来扩展这些研究。此外,我们的结果表明,狼疮颅骨复合体中基于形状的性别二态性在形态学上可能比以前的研究人员认识到的更广泛。我们的结果表明,大小和形状二态性可以在小样本中检测到,并且可能在哺乳动物形态中分离。这一结果特别值得注意,因为它意味着可能需要完善异速生长假设检验,以寻求解释表型性二态性。我们在这项研究中采用的方法是完全通用的,可以应用于广泛的生物材料,并且可以促进对各种形态学/表型假设的快速评估。
更新日期:2020-09-03
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