Beyond hinges and spires: A critical examination of archaeomalacological quantification methodologies using coral reef molluscan assemblages from Jiigurru (Lizard Island Group), northern Great Barrier Reef

Quantification, as one of the pillars of the zooarchaeological subdiscipline, is an invaluable component of the toolkit researchers use to study past people-animal interactions. Despite being the subject of rigorous (zoo)archaeological debate, the calculation of Minimum Number of Individuals (MNI) values remains one of the main methods of quantifying the relative abundance of taxa within faunal assemblages. Choosing the appropriate quantification protocol to calculate the MNI of archaeological invertebrate assemblages can be challenging due to regional taxonomic considerations and the myriad of quantification methodologies and frameworks available in the global archaeomalacological literature. In an Australian context, methodologies for quantifying coral reef molluscan assemblages have not been explicitly evaluated. Using archaeological molluscan assemblages from two midden sites (Freshwater Bay Midden and Mangrove Beach Headland Midden) on the northern Great Barrier Reef island group of Jiigurru (the Lizard Island Group), we critically examine two commonly adopted archaeomalacological quantification methodologies: the NRE MNI and tMNI protocols. The NRE MNI methodology uses one to two non-repetitive elements (NREs) of molluscs, whilst the tMNI protocol includes a wider range of elements akin to vertebrate MNI quantification methodologies. Through a comparison of taxa abundances and statistical analyses, results show that the tMNI protocol, with some modification, is best suited for the Jiigurru assemblages. Higher MNI values and an increased assemblage diversity, evenness, and richness were recorded for the molluscan assemblages at both midden sites when the tMNI protocol was applied. This study foregrounds the importance of data transparency when reporting quantification protocols and outcomes to ensure the highest degree of data quality, replicability, and usability.