The measurement of stable isotopes in ‘bulk’ animal and plant tissues (e.g., muscle or leaf) has become an important tool for studies of functional diversity from organismal to continental scales. In consumers, isotope values reflect their diet, trophic position, physiological state, and geographic location. However, interpretation of bulk tissue isotope values can be confounded by variation in primary producer baseline values and by overlapping values among potential food items. To resolve these issues, biologists increasingly use compound-specific isotope analysis (CSIA), in which the isotope values of monomers that constitute a macromolecule (e.g., amino acids in protein) are measured. In this review, we provide the theoretical underpinnings for CSIA, summarize its methodology and recent applications, and identify future research directions. The key principle is that some monomers are reliably routed directly from the diet into animal tissue, whereas others are biochemically transformed during assimilation. As a result, CSIA of consumer tissue simultaneously provides information about an animal’s nutrient sources (e.g., food items or contributions from gut microbes) and its physiology (e.g., nitrogen excretion mode). In combination, these data clarify many of the confounding issues in bulk analysis and enable novel precision for tracing nutrient and energy flow within and among organisms and ecosystems.

Compound‐specific stable isotope analysis of individual amino acids (CSIA‐AA) has emerged as a transformative approach to estimate consumer trophic positions (TP_CSIA) that are internally indexed to primary producer nitrogen isotope baselines. Central to accurate TP_CSIA estimation is an understanding of beta (β) values—the differences between trophic and source AA δ¹⁵N values in the primary producers at the base of a consumers’ food web. Growing evidence suggests higher taxonomic and tissue‐specific β value variability than typically appreciated. This meta‐analysis fulfils a pressing need to comprehensively evaluate relevant sources of β value variability and its contribution to TP_CSIA uncertainty. We first synthesized all published primary producer AA δ¹⁵N data to investigate ecologically relevant sources of variability (e.g. taxonomy, tissue type, habitat type, mode of photosynthesis). We then reviewed the biogeochemical mechanisms underpinning AA δ¹⁵N and β value variability. Lastly, we evaluated the sensitivity of TP_CSIA estimates to uncertainty in mean β_Glx‐Phe values and Glx‐Phe trophic discrimination factors (TDF_Glx‐Phe). We show that variation in β_Glx‐Phe values is two times greater than previously considered, with degree of vascularization, not habitat type (terrestrial vs. aquatic), providing the greatest source of variability (vascular autotroph = −6.6 ± 3.4‰; non‐vascular autotroph = +3.3 ± 1.8‰). Within vascular plants, tissue type secondarily contributed to β_Glx‐Phe value variability, but we found no clear distinction among C₃, C₄ and CAM plant β_Glx‐Phe values. Notably, we found that vascular plant β_Glx‐Lys values (+2.5 ± 1.6‰) are considerably less variable than β_Glx‐Phe values, making Lys a useful AA tracer of primary production sources in terrestrial systems. Our multi‐trophic level sensitivity analyses demonstrate that TP_CSIA estimates are highly sensitive to changes in both β_Glx‐Phe and TDF_Glx‐Phe values but that the relative influence of β values dissipates at higher trophic levels. Our results highlight that primary producer β values are integral to accurate trophic position estimation. We outline four key recommendations for identifying, constraining and accounting for β value variability to improve TP_CSIA estimation accuracy and precision moving forward. We must ultimately expand libraries of primary producer AA δ¹⁵N values to better understand the mechanistic drivers of β value variation.

Biodiversity collections are experiencing a renaissance fueled by the intersection of informatics, emerging technologies, and the extended use and interpretation of specimens and archived databases. In this article, we explore the potential for transformative research in ecology integrating biodiversity collections, stable isotope analysis (SIA), and environmental informatics. Like genomic DNA, SIA provides a common currency interpreted in the context of biogeochemical principles. Integration of SIA data across collections allows for evaluation of long-term ecological change at local to continental scales. Challenges including the analysis of sparse samples, a lack of information about baseline isotopic composition, and the effects of preservation remain, but none of these challenges is insurmountable. The proposed research framework interfaces with existing databases and observatories to provide benchmarks for retrospective studies and ecological forecasting. Collections and SIA add historical context to fundamental questions in freshwater ecological research, reference points for ecosystem monitoring, and a means of quantitative assessment for ecosystem restoration.