Which pulse is it? Identifying archaeological legumes seeds by means of biometric measurements and geometric morphometrics

Abstract

The taxonomic identification of archaeological seeds of certain cultivated pulses is challenging due to a combination of low interspecific morphological differences and large intraspecific variability. This study develops biometric and morphogeometric models that establish which species have more similarities with the doubtful archaeological pulse seeds. The species under study are: Lathyrus cicera/sativus (grass pea), Lens culinaris (lentil), Pisum sativum (pea), Vicia ervilia (bitter vetch), V. faba (broad bean), and V. sativa (vetch). The two models were trained on data from three different types of samples: contemporary uncharred seeds, contemporary charred seeds, and archaeological seeds. The last one consists of finds unearthed on Bronze and Iron Age sites throughout the Western Mediterranean and can be subdivided into two groups: clearly identified taxa and uncertain taxa. The biometric model resorts to the measurements of length, width and thickness of the three types of seeds so as to establish a discriminant linear model applicable to the uncertain archaeological seeds. The morphogeometric model is based on the shape of the three types of seeds gleaned from geometric morphometry. The uncertain archaeological seeds were then classified by means of a Linear Discriminant model based on shape descriptors. This study first assessed the accuracy of the two models stemming from observations of clearly defined contemporary and archaeological taxa. The results indicate that the models, in particular the morphogeometric, yield high predictive rates. These models therefore offer the possibility of re-identifying the taxa of doubtful archaeological seeds with a high degree of confidence and a minimal margin of error. The positive outcome of these models thus paves the way to more accurate determinations of archaeological legume seeds that heretofore have remained unidentified by traditional methods.