Morphometric analysis of Middle Permian Eopolydiexodina and Monodiexodina species from the Dalan Formation, Zard‐Kuh Mountains, Zagros, Iran: An integration of traditional approach and quantitative analyses to identify fusulinid species

Abstract

Eopolydiexodina and Monodiexodina individuals from the Middle Permian lower Dalan Member of the Dalan Formation in the Zard‐Kuh Mountains, southwest Iran, are quantitatively studied using histograms, normal probability plots, test of normality, and parametric (PCA and PCoA) and non‐parametric (NMDS) ordination techniques, in order to determine the accuracy of previously identified Eopolydiexodina and Monodiexodina species based on traditionally defined fusulinid parameters. Specimens of the genus Eopolydiexodina recorded in the lower Dalan Member have been previously assigned to one species and those of Monodiexodina are subdivided into one species and one subspecies. In the present study, 35 specimens from an Eopolydiexodina‐bearing bed in unit 11 and 40 individuals from a Monodiexodina‐bearing bed in unit 17 of the lower Dalan Member are selected. Observations of the histograms, normal probability plots, and test of normality for all the measured variables reveal that Eopolydiexodina and Monodiexodina individuals follow generally a normal distribution. The results of the principal component analysis (PCA), principle coordinate analysis (PCoA), and non‐metric multidimensional scaling (NMDS) suggest that the distribution of the studied individuals cannot be divided into different groups and only one species for Eopolydiexodina (E. aff. persica) and one species for Monodiexodina (M. aff. kattaensis) are identifiable. This study provides an example to show how the identification and definition of fusulinid species can be well constrained using both the traditional description approach and modern morphometric techniques together.

An image dataset of fusulinid foraminifera generated with the aid of deep learning

“…With enough images involved, quantitative morphological analyses (Arefifard, 2019; Huang, 2011; Shi, 2021; Shi & MacLeod, 2016) and automatic identification systems (Mitra et al, 2019; Pires de Lima et al, 2020) can help improve the consistency as well as efficiency of identification practices. An example is ‘Endless Forams’ (Hsiang et al, 2019), an image dataset that provides over 34,000 images of 35 modern foraminifera species.…”