Kim Vane, Matthew R. D. Cobain, Thomas Larsen

Natural and anthropogenic stressors alter the composition, biomass, and nutritional quality of primary producers and microorganisms, the basal organisms that synthesise the biomolecules essential for metazoan growth and survival (i.e. basal resources). Traditional biomarkers have provided valuable insight into the spatiotemporal dynamics of basal resource use, but lack specificity in identifying multiple basal organisms, can be confounded by environmental and physiological processes, and do not always preserve in tissues over long timescales. Carbon stable isotope ratios of essential amino acids (δ13C-EAA) show remarkable promise in identifying and distinguishing clades of basal organisms with unique δ13C-EAA fingerprints that are independent of trophic processing and environmental variability, providing unparalleled potential in their application. Understanding the biochemical processes that underpin δ13C-AA data is crucial however for holistic and robust inferences in ecological applications. This comprehensive methodological review conceptualises for the first time these mechanistic underpinnings that drive δ13C-EAA fingerprints among basal organisms and incorporates δ13C values of non-essential amino acids that are generally overlooked in ecological studies, despite the gain of metabolic information. We conduct meta-analyses of published data to test hypothesised AA-specific isotope fractionations among basal organism clades, demonstrating that phenylalanine separates vascular plant δ13C-EAA fingerprints, which strongly covaries with their phylogeny. We further explore the utility of non-essential AAs in separating dietary protein sources of archaeological humans, showing the differences in metabolic information contained within different NEAAs. By scrutinising the many methodologies that are applied in the field, we highlight the absence of standardised analytical protocols, particularly in sample pretreatments leading to biases; inappropriate use of statistical methods; and reliance on unsuitable training data. To unlock the full potential of δ13C-EAA fingerprints, we provide in-depth explanations on knowledge gaps, pitfalls, and optimal practices in this complex but powerful approach for assessing ecosystem change across spatiotemporal scales.