Application of modern mathematical methods for species discrimination in the water fleas (Cladocera: Branchiopoda) that appear similar to the human eye: case of Bosmina (Bosmina) longirostris (O.F. Muller, 1776) from European Eurasia and Sakhalin Island

Intraspecific morphological variability presents a complex challenge for biological systematics and biomonitoring, particularly for organisms with high phenotypic plasticity, such as zooplankton. Morphological differences between individuals of the water flea species Bosmina longirostris (Crustacea: Cladocera) are difficult to distinguish visually, parthenogenetic females look morphologically uniform within the species; nevertheless, they demonstrate differences attributable to their geographic origin and developmental stage. A reference dataset of microscopic images was created for the study, including populations from two geographically separated regions (seven ones from European Russia and seven ones from Sakhalin Island in the Pacific Ocean (Far East of Russia) and two age groups, demonstrating the ability of a neural network classify to successfully the intraspecific morphological variation. This study demonstrates that deep learning methods are prospective for the detection and understanding of fine morphological intraspecific differences in the cladocerans.