Topological Data Analysis and Multiple Kernel Learning for Species Identification of Modern and Archaeological Small Ruminants

The faunal remains from numerous Holocene archaeological sites across southwest Asia frequently include the bones of various wild and domestic ungulates, such as sheep, goats, ibexes, roe deer and gazelles. These assemblages may provide insight into hunting and animal husbandry strategies and offer palaeoecological information on ancient human societies. However, the skeletons of these taxa are highly similar in appearance, which presents a challenge for accurate identification based on their bones. This paper presents a case study to test the potential of topological data analysis (TDA) and multiple kernel learning (MKL) for inter-specific identification of 150 3D astragali belonging to modern and archaeological specimens. The joint application of TDA and MKL demonstrated remarkable efficacy in accurately identifying wild species, with a correct identification rate of approximately 90%. In contrast, the identification of domestic species exhibited a lower success rate, at approximately 60%. This low rate of identification of sheep and goat species is attributed to the morphological variability of domestic breeds. Moreover, while these methods assist in clearly identifying wild taxa from one another, they also highlight their morphological diversity. In this context, TDA and MKL could be invaluable for investigating intra-specific variability in domestic and wild animals. These methods offer a means of expanding our understanding of past domestic animal selection practices and techniques. They also facilitate an investigation into the morphological evolution of wild animal populations over time.