Morphometrics and machine learning discrimination of the middle Eocene radiolarian species Podocyrtis chalara, Podocyrtis goetheana and their morphological intermediates

We present various approaches to distinguish the middle Eocene species Podocyrtis chalara and Podocyrtis goe-theana, 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 discrimi-nation 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 mor-phospecies 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.

Automated summary

This study presents various approaches to distinguish two middle Eocene species, Podocyrtis chalara and Podocyrtis goe-theana, using traditional morphological variables and machine learning methods. The results indicate that neural network approaches can achieve high accuracy in morphological classification.