Application of extended depth of field 3D imagery to tackle the challenges of cryptic species: a use case in the genus Betiscoides Sjöstedt, 1924 (Orthoptera, Caelifera, Lentulidae) and its taxonomic implications

Abstract

Discovering and handling cryptic diversity among species challenges taxonomists around the world. This is particularly true for the most diverse animal class – the insects – with cryptic diversity, apart from vast species numbers, being one of the main factors that hamper the description of new species. The biodiversity hotspot Cape Floristic Region of South Africa harbors many endemic and yet undescribed insect species, inter alia, Orthoptera. In this study, extended depth of field and 3D imagery enabled for a novel assessment of the external morphological characteristics used for defining and describing the genetically highly diverse genus Betiscoides Sjösdtedt, 1924, leading to a new definition of the genus’ characteristics as well as a revision of character traits of the known species. Two new species are described and a key to all five recognized Betiscoides species is provided. Application standards are derived to enable replicable and reliable image acquisition and measuring. These findings shall contribute to promote efforts being made to establish image based taxonomic identification for researchers worldwide. High-resolution images provide the basis to train deep learning algorithms/ tools, to detect the smallest differences in highly morphologically alike species, and to implement this knowledge in global species monitoring and conservation action to prevent further species loss.