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Abstract 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. 

Abstract 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_CSIAestimation 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‐specificβvalue variability than typically appreciated.This meta‐analysis fulfils a pressing need to comprehensively evaluate relevant sources ofβvalue variability and its contribution to TP_CSIAuncertainty. 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 andβvalue variability. Lastly, we evaluated the sensitivity of TP_CSIAestimates to uncertainty in meanβ_Glx‐Phevalues and Glx‐Phe trophic discrimination factors (TDF_Glx‐Phe).We show that variation inβ_Glx‐Phevalues 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 toβ_Glx‐Phevalue variability, but we found no clear distinction among C₃, C₄and CAM plantβ_Glx‐Phevalues. Notably, we found that vascular plantβ_Glx‐Lysvalues (+2.5 ± 1.6‰) are considerably less variable thanβ_Glx‐Phevalues, making Lys a useful AA tracer of primary production sources in terrestrial systems. Our multi‐trophic level sensitivity analyses demonstrate that TP_CSIAestimates are highly sensitive to changes in bothβ_Glx‐Pheand TDF_Glx‐Phevalues but that the relative influence ofβvalues dissipates at higher trophic levels.Our results highlight that primary producerβvalues are integral to accurate trophic position estimation. We outline four key recommendations for identifying, constraining and accounting forβvalue variability to improve TP_CSIAestimation accuracy and precision moving forward. We must ultimately expand libraries of primary producer AA δ¹⁵N values to better understand the mechanistic drivers ofβvalue variation.