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RationaleThe sampling of sequential, annually formed bone growth layers for stable carbon (δ(13)C values) and nitrogen (δ(15)N values) isotope analysis (SIA) can provide a time series of foraging ecology data. To date, no standard protocol exists for the pre-SIA treatment of cortical samples taken from fresh, modern, bones.MethodsBased on the SIA of historical bone, it is assumed that fresh bone samples must be pre-treated with acid prior to SIA. Using an elemental analyzer coupled to an isotope ratio mass spectrometer to measure stable carbon and nitrogen ratios, we tested the need to acidify cortical bone powder with 0.25 M HCl prior to SIA to isolate bone collagen for the determination of δ(13)C and δ(15)N values. We also examined the need for lipid extraction to remove potential biases related to δ(13)C analysis, based on a C:N ratio threshold of 3.5.ResultsIt was found that acidification of micromilled cortical bone samples from marine turtles does not affect their δ(15)N values, and the small effect acidification has on δ(13)C values can be mathematically corrected for, thus eliminating the need for pre-SIA acidification of cortical bone. The lipid content of the cortical bone samples was low, as measured by their C:N ratios, indicating that lipid extracting cortical bone samples from modern marine turtles is unnecessary.ConclusionsWe present a standard protocol for testing fresh, modern cortical bone samples prior to SIA, facilitating direct comparison of future studies. Based on the results obtained from marine turtle bones, pre-acidification and lipid removal of cortical bone are not recommended. This is especially useful as there is frequently not enough bone material removed via micromilling of sequential growth layers to accommodate both acid treatment and SIA.

Compound‐specific stable isotope analysis of individual amino acids (CSIA‐AA) has emerged as a transformative approach to estimate consumer trophic positions (TPCSIA) that are internally indexed to primary producer nitrogen isotope baselines. Central to accurate TPCSIA 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 TPCSIA 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 TPCSIA estimates to uncertainty in mean βGlx‐Phe values and Glx‐Phe trophic discrimination factors (TDFGlx‐Phe).We show that variation in βGlx‐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 βGlx‐Phe value variability, but we found no clear distinction among C3, C4 and CAM plant βGlx‐Phe values. Notably, we found that vascular plant βGlx‐Lys values (+2.5 ± 1.6‰) are considerably less variable than βGlx‐Phe values, making Lys a useful AA tracer of primary production sources in terrestrial systems. Our multi‐trophic level sensitivity analyses demonstrate that TPCSIA estimates are highly sensitive to changes in both βGlx‐Phe and TDFGlx‐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 TPCSIA 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. [ABSTRACT FROM AUTHOR]