The power and pitfalls of amino acid carbon stable isotopes for tracing origin and use of basal resources in food webs

Abstract Natural and anthropogenic stressors alter the composition, biomass, and nutritional quality of primary producers and microorganisms, the basal organisms that synthesize 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 (δ 13 C‐EAA) show remarkable promise in identifying and distinguishing clades of basal organisms with unique δ 13 C‐EAA fingerprints that are independent of trophic processing and environmental variability, providing unparalleled potential in their application. Understanding the biochemical processes that underpin δ 13 C‐AA data is crucial, however, for holistic and robust inferences in ecological applications. This comprehensive methodological review, for the first time, conceptualizes these mechanistic underpinnings that drive δ 13 C‐EAA fingerprints among basal organisms and incorporates δ 13 C 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 hypothesized AA‐specific isotope fractionations among basal organism clades, demonstrating that phenylalanine separates vascular plant δ 13 C‐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 scrutinizing the many methodologies that are applied in the field, we highlight the absence of standardized 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 δ 13 C‐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.