We present various approaches to distinguish the middle Eocene species Podocyrtis chalara and Podocyrtis goetheana, which are end members of a trajectory of phenotypic change, and their intermediate morphogroups. We constructed a set of thirteen traditional morphological variables to classify the entire morphological variability encompassed by the two morphospecies and their intermediates Podocyrtis sp. cf. P. chalara and Podocyrtis sp. cf. P. goetheana. We used two methods of classification, namely Linear Discriminant Analysis (LDA) and machine learning using artificial neural networks. LDA performed on the morphometric data reveals a good discrimination for P. chalara, P. goetheana and Podocyrtis sp. cf. P. goetheana, but not for Podocyrtis sp. cf. P. chalara. We used three approaches of machine learning based on different neural networks: a Convolutional Neural Network (CNN) and two Spiking Neural Networks (SNNs). Each of these neural networks was trained based on classified images of the two morphospecies and their morphological intermediates, thus constituting a different set of input data than the morphometric dataset for LDA. The neural network approaches identified the same three morphospecies recognized by LDA from a dataset of traditional measurements, i.e. P. chalara, P. goetheana and Podocyrtis sp. cf. P. goetheana, with up to 92% accuracy. Our results highlight the great potential and promising perspectives of machine learning and neural networks in the application of image-based object recognition for morphological classification, which may also contribute to more objective taxonomic decisions.

我们提出了各种方法来区分始新世中期物种Podocyrtis chalara和Podocyrtis goetheana，它们是表型变化轨迹的末端成员及其中间形态群。我们构建了一组十三个传统形态变量来对两个形态种及其中间体Podocyrtis sp所涵盖的整个形态变异性进行分类。参见 P. chalara和Podocyrtis sp. 参见 P.歌西那。我们使用了两种分类方法，即线性判别分析（LDA）和使用人工神经网络的机器学习。对形态测量数据进行的 LDA 揭示了对P. chalara、P. goetheana和Podocyrtis sp的良好区分。参见 P. goetheana，但不适用于Podocyrtis sp。参见 P.查拉拉。我们使用了三种基于不同神经网络的机器学习方法：一个卷积神经网络（CNN）和两个尖峰神经网络（SNN）。这些神经网络中的每一个都是基于两种形态种类及其形态中间体的分类图像进行训练的，从而构成了与 LDA 形态测量数据集不同的输入数据集。神经网络方法从传统测量数据集中识别出 LDA 识别的相同三种形态种类，即P。查拉拉，P . goetheana和Podocyrtis sp. 参见 P. goetheana，准确率高达 92%。我们的结果凸显了机器学习和神经网络在基于图像的对象识别应用于形态分类方面的巨大潜力和前景，这也可能有助于更客观的分类决策。